Impact of organic fertilizers on the quality of mango (Mangifera indica L.) var. ‘Kent’ during physiological maturity and commercial maturity

1 Project Setup

library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
library(factoextra)
library(corrplot)
library(car)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd(), "\n")
Project:  C:/INIA/GIT/prochira_abonos_mango 
session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11 ucrt)
 os       Windows 11 x64 (build 26100)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Peru.utf8
 ctype    Spanish_Peru.utf8
 tz       America/Lima
 date     2025-06-17
 pandoc   3.4 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
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 [1] C:/Users/INIA/AppData/Local/R/win-library/4.5
 [2] C:/Program Files/R/R-4.5.0/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

2 Import data

Data was imported from the field book evaluated during the 2022-2023 growing season. The evaluations focused on mango fruits of the ‘Kent’ variety at two stages: physiological maturity and commercial maturity.

url <- "https://docs.google.com/spreadsheets/d/1cjWrS-EVcII85c-l_NuEfTpjhVMI156e8REM9GDVP_w/edit?gid=95386135#gid=95386135"

gs <- url %>% 
  as_sheets_id()

tratamiento <- gs %>% 
  range_read("tratamientos") %>% 
  rename_with(~ tolower(.))

rendimiento <- gs %>% 
  range_read("rendimiento") %>% 
  rename_with(~ tolower(.)) 

fisio <- gs %>% 
  range_read("fisio") %>% 
  rename_with(~ tolower(.)) %>% 
  merge(., tratamiento) %>% 
  dplyr::select(tratamiento,compost, biol,everything()) %>% 
  merge(., rendimiento) %>% 
  mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>% 
  rename(treat = tratamiento
         , repetition = repeticion
         , composts = compost)

str(fisio)
## 'data.frame':    405 obs. of  15 variables:
##  $ treat     : Factor w/ 9 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ repetition: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nplantas  : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
##  $ composts  : Factor w/ 3 levels "0","5","15": 1 1 1 1 1 1 1 1 1 1 ...
##  $ biol      : Factor w/ 3 levels "0","5","10": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nfrutos   : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
##  $ pcfmf     : num  90 70 60 40 70 70 40 60 30 80 ...
##  $ ffmf      : num  13.2 12 10 10.2 12.8 11.6 10.8 11.9 11 11.2 ...
##  $ cifmf     : num  2 2 2 2 2 1.5 2 2 2.5 2.5 ...
##  $ ssfmf     : num  8.8 8.6 8.5 8.2 8.6 9.8 9.7 9.4 9.2 8.5 ...
##  $ phfmf     : num  2.6 2.55 2.52 2.58 2.55 ...
##  $ atfmf     : num  1.39 1.38 1.2 1.12 1.35 1.49 1.29 1.52 1.54 1.42 ...
##  $ msfmf     : num  19.1 19.1 19.1 19.1 19.1 ...
##  $ imf       : num  6.33 6.23 7.08 7.32 6.37 6.58 7.52 6.18 5.97 5.99 ...
##  $ rpp       : num  51.3 51.3 51.3 51.3 51.3 52.4 52.4 52.4 52.4 52.4 ...

consumo <- gs %>% 
  range_read("consumo") %>% 
  rename_with(~ tolower(.)) %>%
  merge(., tratamiento) %>% 
  dplyr::select(tratamiento,compost, biol,everything()) %>% 
  mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>% 
    rename(treat = tratamiento
         , repetition = repeticion
         , composts = compost
         , n_fruits = nfrutos)

glimpse(consumo)
## Rows: 135
## Columns: 12
## $ treat      <fct> T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0,…
## $ composts   <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5,…
## $ biol       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ repetition <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1,…
## $ n_fruits   <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,…
## $ pdfmc      <dbl> 6.68, 6.78, 7.02, 6.78, 6.12, 6.62, 6.91, 7.04, 6.45, 6.50,…
## $ ffmc       <dbl> 3.0, 3.0, 4.0, 3.8, 4.2, 4.0, 3.6, 3.0, 3.0, 3.0, 3.0, 3.4,…
## $ cifmc      <dbl> 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.5, 3.0, 3.0, 3.0, 3.5, 3.0,…
## $ ssfmc      <dbl> 14.6, 15.6, 14.8, 15.0, 14.0, 14.4, 15.5, 15.1, 15.2, 15.2,…
## $ phfmc      <dbl> 4.22, 4.19, 4.21, 4.15, 4.26, 4.34, 4.35, 4.34, 4.30, 4.38,…
## $ atfmc      <dbl> 0.600, 0.700, 0.700, 0.500, 0.500, 0.605, 0.615, 0.625, 0.6…
## $ imf        <dbl> 24.33333, 22.28571, 21.14286, 30.00000, 28.00000, 23.80165,…
rendimiento %>% kable(caption = "Yield related traits")
Yield related traits
tratamiento repeticion nplantas rpp
T0 1 1 51.30
T0 1 2 52.40
T0 1 3 49.20
T0 2 1 43.40
T0 2 2 45.24
T0 2 3 46.06
T0 3 1 51.72
T0 3 2 51.10
T0 3 3 50.72
T1 1 1 59.00
T1 1 2 56.80
T1 1 3 58.56
T1 2 1 76.20
T1 2 2 74.00
T1 2 3 75.76
T1 3 1 107.00
T1 3 2 106.20
T1 3 3 107.00
T2 1 1 121.40
T2 1 2 119.20
T2 1 3 120.70
T2 2 1 91.60
T2 2 2 93.20
T2 2 3 92.58
T2 3 1 100.60
T2 3 2 102.60
T2 3 3 102.14
T3 1 1 47.60
T3 1 2 50.88
T3 1 3 47.80
T3 2 1 36.00
T3 2 2 34.44
T3 2 3 32.88
T3 3 1 40.48
T3 3 2 39.40
T3 3 3 39.92
T4 1 1 53.40
T4 1 2 54.70
T4 1 3 56.60
T4 2 1 56.48
T4 2 2 56.60
T4 2 3 56.46
T4 3 1 50.40
T4 3 2 51.00
T4 3 3 50.20
T5 1 1 107.40
T5 1 2 109.04
T5 1 3 107.48
T5 2 1 82.60
T5 2 2 82.28
T5 2 3 82.00
T5 3 1 63.56
T5 3 2 61.40
T5 3 3 63.66
T6 1 1 74.50
T6 1 2 76.60
T6 1 3 71.90
T6 2 1 123.40
T6 2 2 120.60
T6 2 3 120.68
T6 3 1 72.76
T6 3 2 75.60
T6 3 3 73.40
T7 1 1 110.00
T7 1 2 109.40
T7 1 3 106.20
T7 2 1 104.20
T7 2 2 105.00
T7 2 3 104.40
T7 3 1 107.20
T7 3 2 108.40
T7 3 3 109.40
T8 1 1 119.48
T8 1 2 118.30
T8 1 3 120.10
T8 2 1 126.48
T8 2 2 128.20
T8 2 3 126.80
T8 3 1 94.20
T8 3 2 94.20
T8 3 3 94.30

fisio %>% kable(caption = "Evaluation of fruits at fisiological maturity")
Evaluation of fruits at fisiological maturity
treat repetition nplantas composts biol nfrutos pcfmf ffmf cifmf ssfmf phfmf atfmf msfmf imf rpp
T0 1 1 0 0 1 90 13.2 2.0 8.80 2.602942 1.390 19.060 6.33 51.30
T0 1 1 0 0 2 70 12.0 2.0 8.60 2.548103 1.380 19.060 6.23 51.30
T0 1 1 0 0 3 60 10.0 2.0 8.50 2.520683 1.200 19.060 7.08 51.30
T0 1 1 0 0 4 40 10.2 2.0 8.20 2.575522 1.120 19.060 7.32 51.30
T0 1 1 0 0 5 70 12.8 2.0 8.60 2.548103 1.350 19.060 6.37 51.30
T0 1 2 0 0 1 70 11.6 1.5 9.80 2.877140 1.490 18.400 6.58 52.40
T0 1 2 0 0 2 40 10.8 2.0 9.70 2.849720 1.290 18.400 7.52 52.40
T0 1 2 0 0 3 60 11.9 2.0 9.40 2.767461 1.520 18.400 6.18 52.40
T0 1 2 0 0 4 30 11.0 2.5 9.20 2.986819 1.540 18.400 5.97 52.40
T0 1 2 0 0 5 80 11.2 2.5 8.50 2.520683 1.420 18.400 5.99 52.40
T0 1 3 0 0 1 80 12.0 2.5 9.60 2.822300 1.430 18.520 6.71 49.20
T0 1 3 0 0 2 50 12.0 2.0 8.00 2.383584 1.480 18.520 5.41 49.20
T0 1 3 0 0 3 80 11.6 2.0 8.00 2.383584 1.250 18.520 6.40 49.20
T0 1 3 0 0 4 70 11.0 2.0 8.50 2.520683 1.420 18.520 5.99 49.20
T0 1 3 0 0 5 60 10.0 2.0 8.60 2.548103 1.350 18.520 6.37 49.20
T0 2 1 0 0 2 60 9.4 2.0 8.40 2.383584 1.560 18.540 5.38 43.40
T0 2 1 0 0 3 80 9.4 2.0 8.80 2.602942 1.480 18.540 5.95 43.40
T0 2 1 0 0 4 80 12.6 2.5 8.20 2.602942 1.350 18.540 6.07 43.40
T0 2 1 0 0 5 60 11.0 2.0 8.30 2.630362 1.240 18.540 6.69 43.40
T0 2 1 0 0 1 80 9.8 2.0 8.00 2.383584 1.420 18.540 5.63 43.40
T0 2 2 0 0 2 60 12.6 2.5 9.10 2.877140 1.190 20.420 7.65 45.24
T0 2 2 0 0 3 50 10.2 2.0 9.20 2.794881 1.570 20.420 5.86 45.24
T0 2 2 0 0 4 80 10.0 2.5 8.20 2.383584 1.540 20.420 5.32 45.24
T0 2 2 0 0 1 70 9.8 2.5 8.00 2.712621 1.150 20.420 6.96 45.24
T0 2 2 0 0 5 20 12.2 2.5 8.40 2.904560 1.480 20.420 5.68 45.24
T0 2 3 0 0 1 60 9.2 2.5 8.00 2.383584 1.250 18.450 6.40 46.06
T0 2 3 0 0 2 10 10.0 2.5 9.20 2.931980 1.260 18.450 7.30 46.06
T0 2 3 0 0 3 80 12.0 2.5 8.40 2.931980 1.280 18.450 6.56 46.06
T0 2 3 0 0 5 70 10.0 2.0 8.40 2.657782 1.350 18.450 6.22 46.06
T0 2 3 0 0 4 80 9.8 2.0 9.20 2.712621 1.240 18.450 7.42 46.06
T0 3 1 0 0 2 20 12.2 2.0 8.45 2.506973 1.360 20.880 6.21 51.72
T0 3 1 0 0 3 80 12.4 2.0 8.35 2.479553 1.480 20.880 5.64 51.72
T0 3 1 0 0 5 50 13.0 2.0 8.45 2.506973 1.520 20.880 5.56 51.72
T0 3 1 0 0 1 20 13.4 1.5 8.65 2.561812 1.450 20.880 5.97 51.72
T0 3 1 0 0 4 60 12.6 2.0 8.55 2.534393 1.490 20.880 5.74 51.72
T0 3 2 0 0 1 70 11.8 3.0 8.00 2.383584 1.240 19.260 6.45 51.10
T0 3 2 0 0 2 60 11.6 2.5 8.00 2.383584 1.260 19.260 6.35 51.10
T0 3 2 0 0 5 30 11.4 2.5 9.00 2.657782 1.320 19.260 6.82 51.10
T0 3 2 0 0 3 50 12.8 2.0 8.00 2.383584 1.280 19.260 6.25 51.10
T0 3 2 0 0 4 80 11.2 3.0 8.20 2.438423 1.350 19.260 6.07 51.10
T0 3 3 0 0 1 10 11.6 2.0 9.00 2.657782 1.340 19.890 6.72 50.72
T0 3 3 0 0 4 30 12.9 2.0 8.00 2.383584 1.310 19.890 6.11 50.72
T0 3 3 0 0 5 20 12.2 2.0 8.00 2.383584 1.200 19.890 6.67 50.72
T0 3 3 0 0 2 50 11.8 2.0 8.40 2.493263 1.560 19.890 5.38 50.72
T0 3 3 0 0 3 20 12.2 2.0 9.00 2.657782 1.290 19.890 6.98 50.72
T1 1 1 5 0 4 40 12.5 2.0 9.20 2.383584 1.290 18.900 7.13 59.00
T1 1 1 5 0 5 50 10.0 2.0 8.00 2.383584 1.360 18.900 5.88 59.00
T1 1 1 5 0 2 60 10.0 2.0 8.20 2.383584 1.340 18.900 6.12 59.00
T1 1 1 5 0 1 80 10.0 2.0 8.50 2.520683 1.280 18.900 6.64 59.00
T1 1 1 5 0 3 30 10.0 2.0 8.40 2.493263 1.250 18.900 6.72 59.00
T1 1 2 5 0 4 90 11.6 2.0 9.00 2.657782 1.280 18.340 7.03 56.80
T1 1 2 5 0 5 80 10.4 2.5 8.80 2.602942 1.190 18.340 7.39 56.80
T1 1 2 5 0 3 30 11.4 2.0 9.60 2.822300 1.290 18.340 7.44 56.80
T1 1 2 5 0 1 70 11.4 2.5 9.50 2.685201 1.470 18.340 6.46 56.80
T1 1 2 5 0 2 50 10.2 2.5 9.40 2.657782 1.280 18.340 7.34 56.80
T1 1 3 5 0 4 60 10.0 2.0 8.80 2.438423 1.350 18.200 6.52 58.56
T1 1 3 5 0 2 80 11.2 2.0 9.20 2.657782 1.260 18.200 7.30 58.56
T1 1 3 5 0 3 70 10.8 2.0 8.60 2.548103 1.290 18.200 6.67 58.56
T1 1 3 5 0 1 90 11.6 2.0 10.00 2.931980 1.240 18.200 8.06 58.56
T1 1 3 5 0 5 70 11.0 2.0 9.80 2.877140 1.150 18.200 8.52 58.56
T1 2 1 5 0 3 70 12.2 2.0 8.00 2.383584 1.280 19.940 6.25 76.20
T1 2 1 5 0 1 90 12.0 2.0 8.80 2.383584 1.350 19.940 6.52 76.20
T1 2 1 5 0 4 40 11.5 2.0 8.50 2.520683 1.270 19.940 6.69 76.20
T1 2 1 5 0 5 70 11.4 2.0 9.70 2.849720 1.240 19.940 7.82 76.20
T1 2 1 5 0 2 60 13.8 2.0 8.20 2.438423 1.320 19.940 6.21 76.20
T1 2 2 5 0 3 50 12.5 2.0 9.10 2.685201 1.480 18.600 6.15 74.00
T1 2 2 5 0 4 30 10.0 1.5 8.20 2.164225 1.250 18.600 6.56 74.00
T1 2 2 5 0 5 80 11.8 2.5 8.20 2.438423 1.340 18.600 6.12 74.00
T1 2 2 5 0 1 60 12.4 1.0 8.60 2.548103 1.250 18.600 6.88 74.00
T1 2 2 5 0 2 20 10.2 2.5 8.80 2.602942 1.260 18.600 6.98 74.00
T1 2 3 5 0 1 50 12.0 2.0 8.20 2.438423 1.350 18.280 6.07 75.76
T1 2 3 5 0 2 80 12.0 2.5 9.00 2.657782 1.310 18.280 6.87 75.76
T1 2 3 5 0 4 70 10.0 2.0 8.40 2.328744 1.250 18.280 6.72 75.76
T1 2 3 5 0 5 10 11.6 2.0 9.80 2.877140 1.290 18.280 7.60 75.76
T1 2 3 5 0 3 20 10.4 2.0 8.70 2.575522 1.250 18.280 6.96 75.76
T1 3 1 5 0 2 60 13.0 2.5 8.00 2.383584 1.350 21.380 5.93 107.00
T1 3 1 5 0 3 80 13.4 2.0 8.25 2.452133 1.340 21.380 6.16 107.00
T1 3 1 5 0 4 70 12.8 2.0 8.20 2.438423 1.390 21.380 5.90 107.00
T1 3 1 5 0 1 80 13.2 2.0 8.35 2.479553 1.280 21.380 6.52 107.00
T1 3 1 5 0 5 60 12.6 2.0 7.30 2.164225 1.410 21.380 5.18 107.00
T1 3 2 5 0 1 50 11.6 2.5 8.10 2.644072 1.350 18.750 6.00 106.20
T1 3 2 5 0 2 80 11.4 2.0 8.40 2.383584 1.240 18.750 6.77 106.20
T1 3 2 5 0 5 30 11.8 2.5 8.50 2.657782 1.350 18.750 6.30 106.20
T1 3 2 5 0 3 70 11.6 2.5 8.80 2.712621 1.250 18.750 7.04 106.20
T1 3 2 5 0 4 40 11.0 2.5 8.60 2.794881 1.380 18.750 6.23 106.20
T1 3 3 5 0 1 80 11.0 2.5 8.90 2.630362 1.340 19.270 6.64 107.00
T1 3 3 5 0 4 80 12.4 2.5 8.00 2.424714 1.200 19.270 6.67 107.00
T1 3 3 5 0 5 50 11.9 2.0 8.20 2.438423 1.190 19.270 6.89 107.00
T1 3 3 5 0 2 50 10.8 2.5 8.20 2.383584 1.260 19.270 6.51 107.00
T1 3 3 5 0 3 90 12.8 2.5 8.10 2.411004 1.270 19.270 6.38 107.00
T2 1 1 15 0 1 60 12.8 2.0 8.80 2.602942 1.340 20.600 6.57 121.40
T2 1 1 15 0 2 70 13.0 2.0 8.60 2.548103 1.150 20.600 7.48 121.40
T2 1 1 15 0 3 50 13.2 2.0 8.50 2.520683 1.290 20.600 6.59 121.40
T2 1 1 15 0 4 80 11.2 2.5 10.20 2.986819 1.420 20.600 7.18 121.40
T2 1 1 15 0 5 70 12.5 2.0 8.40 2.493263 1.470 20.600 5.71 121.40
T2 1 2 15 0 1 90 12.6 2.5 8.90 2.630362 1.280 19.320 6.95 119.20
T2 1 2 15 0 2 90 10.0 2.0 9.00 2.657782 1.290 19.320 6.98 119.20
T2 1 2 15 0 3 100 12.4 2.0 9.00 2.657782 1.280 19.320 7.03 119.20
T2 1 2 15 0 4 90 12.2 2.0 9.50 2.794881 1.190 19.320 7.98 119.20
T2 1 2 15 0 5 80 11.8 2.0 8.80 2.602942 1.240 19.320 7.10 119.20
T2 1 3 15 0 3 60 10.8 2.0 9.00 2.657782 1.250 18.580 7.20 120.70
T2 1 3 15 0 4 10 12.0 2.0 9.40 2.767461 1.150 18.580 8.17 120.70
T2 1 3 15 0 5 80 11.2 2.5 10.50 3.069079 1.280 18.580 8.20 120.70
T2 1 3 15 0 2 50 11.4 2.5 9.00 2.657782 1.290 18.580 6.98 120.70
T2 1 3 15 0 1 70 11.6 2.5 9.00 2.657782 1.160 18.580 7.76 120.70
T2 2 1 15 0 2 70 13.0 2.0 8.60 2.548103 1.280 19.740 6.72 91.60
T2 2 1 15 0 3 60 10.4 2.0 8.50 2.520683 1.290 19.740 6.59 91.60
T2 2 1 15 0 4 50 11.2 2.5 10.20 2.986819 1.280 19.740 7.97 91.60
T2 2 1 15 0 5 70 12.8 2.0 8.40 2.493263 1.190 19.740 7.06 91.60
T2 2 1 15 0 1 70 12.8 2.0 8.80 2.602942 1.350 19.740 6.52 91.60
T2 2 2 15 0 1 70 12.6 1.5 8.90 2.630362 1.340 20.400 6.64 93.20
T2 2 2 15 0 2 90 13.0 2.0 8.60 2.548103 1.420 20.400 6.06 93.20
T2 2 2 15 0 4 30 12.2 2.0 9.00 2.657782 1.220 20.400 7.38 93.20
T2 2 2 15 0 5 60 12.0 2.0 9.00 2.657782 1.270 20.400 7.09 93.20
T2 2 2 15 0 3 100 12.4 2.0 8.90 2.630362 1.240 20.400 7.18 93.20
T2 2 3 15 0 2 50 11.4 2.5 9.80 2.877140 1.280 18.580 7.66 92.58
T2 2 3 15 0 3 40 11.0 2.0 9.00 2.657782 1.190 18.580 7.56 92.58
T2 2 3 15 0 1 70 11.3 2.0 10.00 2.931980 1.290 18.580 7.75 92.58
T2 2 3 15 0 5 80 11.2 2.5 10.20 2.986819 1.290 18.580 7.91 92.58
T2 2 3 15 0 4 70 12.0 2.0 9.00 2.657782 1.260 18.580 7.14 92.58
T2 3 1 15 0 2 40 13.2 2.0 8.45 2.506973 1.290 20.600 6.55 100.60
T2 3 1 15 0 4 30 11.4 2.0 9.80 2.877140 1.360 20.600 7.21 100.60
T2 3 1 15 0 5 80 11.8 2.0 8.25 2.452133 1.150 20.600 7.17 100.60
T2 3 1 15 0 1 80 13.0 2.5 8.65 2.561812 1.280 20.600 6.76 100.60
T2 3 1 15 0 3 60 13.4 2.0 8.35 2.479553 1.350 20.600 6.19 100.60
T2 3 2 15 0 1 100 12.8 2.5 8.75 2.589232 1.160 19.990 7.54 102.60
T2 3 2 15 0 2 60 13.2 2.0 8.45 2.506973 1.240 19.990 6.81 102.60
T2 3 2 15 0 5 90 12.2 2.0 9.00 2.657782 1.240 19.990 7.26 102.60
T2 3 2 15 0 3 100 11.3 2.5 8.75 2.589232 1.360 19.990 6.43 102.60
T2 3 2 15 0 4 40 11.5 2.0 9.00 2.657782 1.290 19.990 6.98 102.60
T2 3 3 15 0 1 80 11.8 2.0 10.40 3.041659 1.200 18.370 8.67 102.14
T2 3 3 15 0 3 70 10.8 2.0 9.40 2.767461 1.300 18.370 7.23 102.14
T2 3 3 15 0 4 60 11.0 2.0 9.25 2.726331 1.360 18.370 6.80 102.14
T2 3 3 15 0 2 80 11.6 2.5 10.40 3.041659 1.320 18.370 7.88 102.14
T2 3 3 15 0 5 90 11.0 2.5 10.35 3.027949 1.340 18.370 7.72 102.14
T3 1 1 0 5 3 90 12.6 2.0 9.00 2.657782 1.420 21.100 6.34 47.60
T3 1 1 0 5 2 60 13.0 2.0 9.00 2.657782 1.320 21.100 6.82 47.60
T3 1 1 0 5 1 80 13.0 2.0 8.90 2.630362 1.400 21.100 6.36 47.60
T3 1 1 0 5 4 50 12.8 2.0 9.10 2.685201 1.150 21.100 7.91 47.60
T3 1 1 0 5 5 80 13.0 2.0 8.00 2.383584 1.420 21.100 5.63 47.60
T3 1 2 0 5 1 100 11.4 2.5 8.30 2.877140 1.380 19.280 6.01 50.88
T3 1 2 0 5 2 60 11.8 2.5 9.00 2.657782 1.390 19.280 6.47 50.88
T3 1 2 0 5 3 70 12.4 2.0 8.90 2.630362 1.160 19.280 7.67 50.88
T3 1 2 0 5 4 60 11.6 2.5 9.00 2.657782 1.280 19.280 7.03 50.88
T3 1 2 0 5 5 90 11.8 2.0 9.00 2.657782 1.290 19.280 6.98 50.88
T3 1 3 0 5 5 70 11.0 2.5 9.50 2.794881 1.240 17.620 7.66 47.80
T3 1 3 0 5 3 40 10.4 2.5 9.00 2.657782 1.360 17.620 6.62 47.80
T3 1 3 0 5 1 40 11.0 2.0 9.00 2.657782 1.320 17.620 6.82 47.80
T3 1 3 0 5 2 80 10.0 2.0 8.80 2.602942 1.340 17.620 6.57 47.80
T3 1 3 0 5 4 40 11.4 2.0 9.00 2.657782 1.250 17.620 7.20 47.80
T3 2 1 0 5 5 40 10.6 2.0 8.90 2.630362 1.350 20.352 6.59 36.00
T3 2 1 0 5 4 50 12.8 2.0 9.10 2.685201 1.290 20.352 7.05 36.00
T3 2 1 0 5 2 80 13.0 2.0 8.70 2.520683 1.200 20.352 7.25 36.00
T3 2 1 0 5 1 60 13.0 2.0 9.50 2.602942 1.280 20.352 7.42 36.00
T3 2 1 0 5 3 90 12.6 2.0 8.70 2.575522 1.270 20.352 6.85 36.00
T3 2 2 0 5 1 80 11.4 2.5 9.80 2.877140 1.340 19.576 7.31 34.44
T3 2 2 0 5 3 60 12.4 2.0 8.90 2.630362 1.250 19.576 7.12 34.44
T3 2 2 0 5 4 90 11.6 2.5 8.20 2.383584 1.390 19.576 5.90 34.44
T3 2 2 0 5 5 50 11.8 2.0 9.60 2.383584 1.350 19.576 7.11 34.44
T3 2 2 0 5 2 50 12.6 2.5 9.20 2.712621 1.280 19.576 7.19 34.44
T3 2 3 0 5 1 80 10.8 2.0 8.80 2.383584 1.240 18.276 7.10 32.88
T3 2 3 0 5 5 70 11.0 2.5 8.50 2.520683 1.340 18.276 6.34 32.88
T3 2 3 0 5 3 60 11.4 2.5 8.90 2.630362 1.290 18.276 6.90 32.88
T3 2 3 0 5 4 50 11.4 2.0 8.90 2.630362 1.350 18.276 6.59 32.88
T3 2 3 0 5 2 50 11.4 2.0 9.40 2.383584 1.280 18.276 7.34 32.88
T3 3 1 0 5 1 90 13.2 2.0 8.65 2.260000 1.320 19.734 6.55 40.48
T3 3 1 0 5 2 90 13.2 2.0 8.35 2.479553 1.280 19.734 6.52 40.48
T3 3 1 0 5 3 80 12.0 2.0 8.55 2.534393 1.350 19.734 6.33 40.48
T3 3 1 0 5 4 60 10.0 2.0 8.95 2.644072 1.360 19.734 6.58 40.48
T3 3 1 0 5 5 60 11.8 2.0 8.00 2.383584 1.420 19.734 5.63 40.48
T3 3 2 0 5 1 100 11.6 3.0 8.00 2.150000 1.400 19.234 5.71 39.40
T3 3 2 0 5 2 60 10.8 2.0 8.40 2.280000 1.250 19.234 6.72 39.40
T3 3 2 0 5 3 80 12.6 2.0 8.00 2.383584 1.260 19.234 6.35 39.40
T3 3 2 0 5 4 40 11.8 2.0 8.30 2.383584 1.290 19.234 6.43 39.40
T3 3 2 0 5 5 80 12.0 2.5 9.00 2.560000 1.350 19.234 6.67 39.40
T3 3 3 0 5 5 50 11.2 2.5 8.35 2.080000 1.350 18.688 6.19 39.92
T3 3 3 0 5 3 50 11.4 2.5 8.20 2.100000 1.260 18.688 6.51 39.92
T3 3 3 0 5 2 50 11.4 2.0 8.00 2.383584 1.250 18.688 6.40 39.92
T3 3 3 0 5 4 60 11.4 2.0 8.00 2.383584 1.380 18.688 5.80 39.92
T3 3 3 0 5 1 80 11.8 2.0 9.20 2.280000 1.370 18.688 6.72 39.92
T4 1 1 0 10 3 60 12.0 2.0 9.20 2.712621 1.240 20.440 7.42 53.40
T4 1 1 0 10 1 90 12.3 2.0 8.60 2.548103 1.220 20.440 7.05 53.40
T4 1 1 0 10 4 60 12.6 2.5 9.20 2.712621 1.210 20.440 7.60 53.40
T4 1 1 0 10 5 80 12.4 2.0 9.00 2.657782 1.290 20.440 6.98 53.40
T4 1 1 0 10 2 90 13.0 2.5 8.40 2.493263 1.230 20.440 6.83 53.40
T4 1 2 0 10 3 60 11.4 2.5 9.60 2.822300 1.140 19.598 8.42 54.70
T4 1 2 0 10 2 70 11.6 2.0 9.80 2.877140 1.250 19.598 7.84 54.70
T4 1 2 0 10 5 80 12.0 2.0 8.80 2.602942 1.350 19.598 6.52 54.70
T4 1 2 0 10 1 70 13.0 2.5 8.60 2.548103 1.280 19.598 6.72 54.70
T4 1 2 0 10 4 90 11.8 2.0 9.00 2.657782 1.110 19.598 8.11 54.70
T4 1 3 0 10 5 60 11.6 2.5 10.00 2.931980 1.120 19.508 8.93 56.60
T4 1 3 0 10 4 50 11.0 2.5 10.20 2.986819 1.140 19.508 8.95 56.60
T4 1 3 0 10 1 50 13.2 2.0 8.60 2.548103 1.340 19.508 6.42 56.60
T4 1 3 0 10 2 70 13.0 2.0 8.80 2.602942 1.150 19.508 7.65 56.60
T4 1 3 0 10 3 90 10.8 2.5 10.40 3.041659 1.160 19.508 8.97 56.60
T4 2 1 0 10 3 50 12.0 2.0 9.20 2.712621 1.250 20.880 7.36 56.48
T4 2 1 0 10 5 80 12.8 2.0 9.00 2.657782 1.320 20.880 6.82 56.48
T4 2 1 0 10 2 90 13.0 2.0 8.40 2.493263 1.230 20.880 6.83 56.48
T4 2 1 0 10 1 90 13.2 2.0 8.60 2.548103 1.220 20.880 7.05 56.48
T4 2 1 0 10 4 60 12.6 2.0 9.20 2.712621 1.290 20.880 7.13 56.48
T4 2 2 0 10 1 70 13.0 2.0 8.60 2.548103 1.110 19.578 7.75 56.60
T4 2 2 0 10 5 80 12.0 2.0 8.80 2.602942 1.150 19.578 7.65 56.60
T4 2 2 0 10 2 70 11.6 2.0 9.80 2.877140 1.120 19.578 8.75 56.60
T4 2 2 0 10 3 50 11.4 2.5 9.60 2.822300 1.230 19.578 7.80 56.60
T4 2 2 0 10 4 70 11.8 2.0 9.00 2.657782 1.140 19.578 7.89 56.60
T4 2 3 0 10 4 90 11.0 2.5 10.20 2.986819 1.340 19.508 7.61 56.46
T4 2 3 0 10 5 70 11.6 2.0 10.00 2.931980 1.360 19.508 7.35 56.46
T4 2 3 0 10 3 80 10.8 1.5 10.40 3.041659 1.320 19.508 7.88 56.46
T4 2 3 0 10 1 60 13.2 2.0 8.00 2.383584 1.180 19.508 6.78 56.46
T4 2 3 0 10 2 80 13.0 2.0 9.00 2.657782 1.290 19.508 6.98 56.46
T4 3 1 0 10 3 60 12.2 2.0 9.00 2.657782 1.380 21.084 6.52 50.40
T4 3 1 0 10 2 50 13.2 2.0 9.50 2.794881 1.360 21.084 6.99 50.40
T4 3 1 0 10 4 80 12.8 2.5 10.00 2.931980 1.390 21.084 7.19 50.40
T4 3 1 0 10 5 80 12.6 2.0 10.00 2.931980 1.240 21.084 8.06 50.40
T4 3 1 0 10 1 70 13.4 2.0 8.45 2.506973 1.340 21.084 6.31 50.40
T4 3 2 0 10 2 70 12.0 2.0 9.00 2.657782 1.150 20.318 7.83 51.00
T4 3 2 0 10 1 80 13.2 2.0 8.45 2.506973 1.340 20.318 6.31 51.00
T4 3 2 0 10 4 80 12.0 2.0 8.80 2.602942 1.180 20.318 7.46 51.00
T4 3 2 0 10 5 40 11.8 2.0 8.65 2.561812 1.350 20.318 6.41 51.00
T4 3 2 0 10 3 50 12.9 2.0 9.45 2.781171 1.420 20.318 6.65 51.00
T4 3 3 0 10 2 80 11.8 2.0 7.85 2.342454 1.390 19.336 5.65 50.20
T4 3 3 0 10 4 70 11.2 2.0 9.80 2.877140 1.410 19.336 6.95 50.20
T4 3 3 0 10 1 60 13.4 3.0 8.95 2.644072 1.360 19.336 6.58 50.20
T4 3 3 0 10 3 60 10.9 2.0 10.00 2.931980 1.380 19.336 7.25 50.20
T4 3 3 0 10 5 70 11.8 2.5 9.85 2.890850 1.250 19.336 7.88 50.20
T5 1 1 5 5 5 50 12.0 2.0 8.50 2.520683 1.280 21.018 6.64 107.40
T5 1 1 5 5 3 60 13.0 2.0 8.60 2.548103 1.340 21.018 6.42 107.40
T5 1 1 5 5 4 80 12.8 2.0 8.40 2.493263 1.220 21.018 6.89 107.40
T5 1 1 5 5 1 90 13.0 2.0 8.40 2.493263 1.280 21.018 6.56 107.40
T5 1 1 5 5 2 70 13.2 2.0 8.00 2.383584 1.350 21.018 5.93 107.40
T5 1 2 5 5 5 70 12.0 2.5 10.20 2.986819 1.280 19.168 7.97 109.04
T5 1 2 5 5 1 70 12.2 2.0 8.60 2.548103 1.290 19.168 6.67 109.04
T5 1 2 5 5 3 60 11.2 2.0 9.20 2.712621 1.400 19.168 6.57 109.04
T5 1 2 5 5 4 60 11.8 2.5 9.50 2.794881 1.300 19.168 7.31 109.04
T5 1 2 5 5 2 50 11.4 2.5 10.00 2.931980 1.369 19.168 7.30 109.04
T5 1 3 5 5 3 60 11.4 2.5 9.00 2.657782 1.361 18.756 6.61 107.48
T5 1 3 5 5 4 70 11.6 2.0 9.00 2.657782 1.150 18.756 7.83 107.48
T5 1 3 5 5 2 80 11.2 2.0 8.60 2.548103 1.270 18.756 6.77 107.48
T5 1 3 5 5 5 70 11.8 2.0 8.60 2.548103 1.180 18.756 7.29 107.48
T5 1 3 5 5 1 90 11.4 2.0 9.20 2.712621 1.290 18.756 7.13 107.48
T5 2 1 5 5 3 50 13.0 2.0 8.60 2.548103 1.350 21.018 6.37 82.60
T5 2 1 5 5 4 80 12.8 2.0 8.40 2.493263 1.360 21.018 6.18 82.60
T5 2 1 5 5 2 70 13.2 2.5 8.00 2.383584 1.320 21.018 6.06 82.60
T5 2 1 5 5 5 70 12.0 2.5 8.50 2.520683 1.280 21.018 6.64 82.60
T5 2 1 5 5 1 90 13.0 2.0 8.40 2.493263 1.300 21.018 6.46 82.60
T5 2 2 5 5 5 70 11.6 2.5 9.00 2.657782 1.340 18.996 6.72 82.28
T5 2 2 5 5 3 50 11.2 2.0 9.00 2.657782 1.250 18.996 7.20 82.28
T5 2 2 5 5 4 80 11.7 2.5 10.20 2.986819 1.380 18.996 7.39 82.28
T5 2 2 5 5 1 70 12.2 2.0 8.60 2.548103 1.290 18.996 6.67 82.28
T5 2 2 5 5 2 50 11.4 2.5 9.00 2.657782 1.260 18.996 7.14 82.28
T5 2 3 5 5 4 50 11.0 2.0 9.00 2.657782 1.160 18.362 7.76 82.00
T5 2 3 5 5 5 40 11.8 2.0 8.60 2.548103 1.150 18.362 7.48 82.00
T5 2 3 5 5 1 50 11.4 2.5 9.20 2.712621 1.390 18.362 6.62 82.00
T5 2 3 5 5 2 90 11.0 2.0 8.60 2.548103 1.410 18.362 6.10 82.00
T5 2 3 5 5 3 90 11.0 2.5 10.60 3.096499 1.320 18.362 8.03 82.00
T5 3 1 5 5 2 60 13.4 2.0 7.85 2.342454 1.320 21.359 5.95 63.56
T5 3 1 5 5 3 50 13.2 2.5 8.45 2.506973 1.350 21.359 6.26 63.56
T5 3 1 5 5 5 50 12.2 2.0 8.35 2.479553 1.340 21.359 6.23 63.56
T5 3 1 5 5 1 70 13.2 2.0 8.25 2.452133 1.300 21.359 6.35 63.56
T5 3 1 5 5 4 90 13.0 2.0 8.25 2.452133 1.360 21.359 6.07 63.56
T5 3 2 5 5 2 60 11.6 2.0 9.85 2.890850 1.250 18.824 7.88 61.40
T5 3 2 5 5 4 90 11.2 2.0 10.10 2.959399 1.220 18.824 8.28 61.40
T5 3 2 5 5 5 60 11.0 2.0 10.10 2.959399 1.210 18.824 8.35 61.40
T5 3 2 5 5 1 80 12.4 2.0 8.45 2.506973 1.280 18.824 6.60 61.40
T5 3 2 5 5 3 60 11.4 2.0 9.10 2.685201 1.270 18.824 7.17 61.40
T5 3 3 5 5 4 60 11.8 2.0 8.85 2.616652 1.350 18.516 6.56 63.66
T5 3 3 5 5 5 90 12.0 2.0 8.45 2.506973 1.360 18.516 6.21 63.66
T5 3 3 5 5 2 70 10.5 2.0 8.45 2.506973 1.350 18.516 6.26 63.66
T5 3 3 5 5 1 80 11.6 1.5 9.10 2.685201 1.310 18.516 6.95 63.66
T5 3 3 5 5 3 70 10.8 2.5 10.00 2.931980 1.380 18.516 7.25 63.66
T6 1 1 5 10 2 90 11.4 2.0 8.60 2.548103 1.290 19.508 6.67 74.50
T6 1 1 5 10 3 90 12.4 2.0 9.00 2.657782 1.320 19.508 6.82 74.50
T6 1 1 5 10 4 50 11.8 2.0 9.20 2.712621 1.310 19.508 7.02 74.50
T6 1 1 5 10 1 80 11.6 2.0 8.80 2.602942 1.280 19.508 6.88 74.50
T6 1 1 5 10 5 80 12.4 2.0 8.40 2.493263 1.250 19.508 6.72 74.50
T6 1 2 5 10 2 90 13.2 2.0 9.60 2.822300 1.340 20.640 7.16 76.60
T6 1 2 5 10 1 90 12.8 2.0 8.80 2.602942 1.360 20.640 6.47 76.60
T6 1 2 5 10 5 40 13.2 2.0 8.30 2.465843 1.260 20.640 6.59 76.60
T6 1 2 5 10 3 80 10.9 2.0 9.50 2.794881 1.410 20.640 6.74 76.60
T6 1 2 5 10 4 80 12.8 2.0 9.40 2.767461 1.250 20.640 7.52 76.60
T6 1 3 5 10 5 60 10.8 2.5 10.20 2.986819 1.220 19.188 8.36 71.90
T6 1 3 5 10 4 80 11.4 2.5 9.60 2.822300 1.240 19.188 7.74 71.90
T6 1 3 5 10 1 50 12.8 2.0 10.00 2.931980 1.310 19.188 7.63 71.90
T6 1 3 5 10 2 90 12.2 2.0 9.90 2.904560 1.240 19.188 7.98 71.90
T6 1 3 5 10 3 60 11.4 2.5 9.90 2.904560 1.150 19.188 8.61 71.90
T6 2 1 5 10 1 50 11.6 2.0 8.80 2.602942 1.220 19.392 7.21 123.40
T6 2 1 5 10 4 90 11.8 2.0 9.20 2.712621 1.220 19.392 7.54 123.40
T6 2 1 5 10 2 60 11.4 2.0 8.60 2.548103 1.310 19.392 6.56 123.40
T6 2 1 5 10 3 70 12.4 2.0 9.00 2.657782 1.240 19.392 7.26 123.40
T6 2 1 5 10 5 50 12.0 2.0 8.40 2.493263 1.190 19.392 7.06 123.40
T6 2 2 5 10 1 90 11.6 2.0 8.80 2.602942 1.170 21.114 7.52 120.60
T6 2 2 5 10 2 70 13.2 2.0 9.60 2.822300 1.150 21.114 8.35 120.60
T6 2 2 5 10 3 80 13.4 2.0 9.50 2.794881 1.260 21.114 7.54 120.60
T6 2 2 5 10 4 80 12.8 2.0 8.70 2.575522 1.250 21.114 6.96 120.60
T6 2 2 5 10 5 90 13.3 2.0 9.60 2.822300 1.290 21.114 7.44 120.60
T6 2 3 5 10 4 90 11.8 2.5 9.50 2.794881 1.280 19.576 7.42 120.68
T6 2 3 5 10 2 80 12.2 2.0 9.60 2.822300 1.340 19.576 7.16 120.68
T6 2 3 5 10 5 70 10.8 2.5 10.20 2.986819 1.250 19.576 8.16 120.68
T6 2 3 5 10 1 60 12.4 2.0 10.00 2.931980 1.350 19.576 7.41 120.68
T6 2 3 5 10 3 40 12.6 2.5 9.80 2.877140 1.260 19.576 7.78 120.68
T6 3 1 5 10 4 50 12.0 2.5 9.10 2.685201 1.350 19.714 6.74 72.76
T6 3 1 5 10 3 90 12.6 2.0 8.85 2.616652 1.260 19.714 7.02 72.76
T6 3 1 5 10 1 80 11.8 2.0 8.65 2.561812 1.280 19.714 6.76 72.76
T6 3 1 5 10 5 60 12.2 2.0 9.00 2.657782 1.340 19.714 6.72 72.76
T6 3 1 5 10 2 90 11.6 2.5 8.45 2.506973 1.290 19.714 6.55 72.76
T6 3 2 5 10 2 90 13.4 2.0 9.45 2.781171 1.310 21.222 7.21 75.60
T6 3 2 5 10 1 50 11.8 2.0 8.65 2.561812 1.320 21.222 6.55 75.60
T6 3 2 5 10 4 80 13.0 2.0 9.00 2.657782 1.290 21.222 6.98 75.60
T6 3 2 5 10 5 90 12.8 2.0 9.00 2.657782 1.420 21.222 6.34 75.60
T6 3 2 5 10 3 80 13.6 2.5 9.35 2.753751 1.280 21.222 7.30 75.60
T6 3 3 5 10 5 80 11.0 2.5 10.10 2.959399 1.280 19.666 7.89 73.40
T6 3 3 5 10 3 90 11.2 2.0 10.00 2.931980 1.220 19.666 8.20 73.40
T6 3 3 5 10 2 80 12.4 2.0 10.00 2.931980 1.160 19.666 8.62 73.40
T6 3 3 5 10 4 40 12.8 2.0 10.00 2.931980 1.240 19.666 8.06 73.40
T6 3 3 5 10 1 60 12.6 2.0 10.00 2.931980 1.140 19.666 8.77 73.40
T7 1 1 15 5 3 80 11.8 2.5 10.40 3.041659 1.290 18.822 8.06 110.00
T7 1 1 15 5 1 90 11.6 2.0 9.80 2.877140 1.220 18.822 8.03 110.00
T7 1 1 15 5 2 80 11.4 2.0 9.60 2.822300 1.240 18.822 7.74 110.00
T7 1 1 15 5 4 60 11.0 2.0 9.60 2.822300 1.180 18.822 8.14 110.00
T7 1 1 15 5 5 90 11.8 2.0 9.80 2.877140 1.150 18.822 8.52 110.00
T7 1 2 15 5 1 50 10.8 2.5 9.80 2.877140 1.140 20.538 8.60 109.40
T7 1 2 15 5 5 80 12.8 2.0 9.00 2.657782 1.340 20.538 6.72 109.40
T7 1 2 15 5 2 80 13.0 2.0 8.80 2.602942 1.160 20.538 7.59 109.40
T7 1 2 15 5 3 80 13.2 2.0 8.00 2.383584 1.170 20.538 6.84 109.40
T7 1 2 15 5 4 90 12.8 2.5 9.00 2.657782 1.320 20.538 6.82 109.40
T7 1 3 15 5 1 80 12.8 2.0 9.00 2.657782 1.350 21.088 6.67 106.20
T7 1 3 15 5 4 80 13.0 2.0 8.20 2.438423 1.320 21.088 6.21 106.20
T7 1 3 15 5 5 70 12.8 2.0 10.00 2.931980 1.360 21.088 7.35 106.20
T7 1 3 15 5 2 50 12.8 2.5 9.00 2.657782 1.360 21.088 6.62 106.20
T7 1 3 15 5 3 70 12.8 2.0 8.00 2.383584 1.280 21.088 6.25 106.20
T7 2 1 15 5 4 40 12.8 2.0 9.00 2.657782 1.280 20.770 7.03 104.20
T7 2 1 15 5 3 80 12.8 2.5 10.40 3.041659 1.310 20.770 7.94 104.20
T7 2 1 15 5 1 90 12.8 2.0 9.80 2.877140 1.280 20.770 7.66 104.20
T7 2 1 15 5 2 80 12.8 2.0 9.60 2.822300 1.290 20.770 7.44 104.20
T7 2 1 15 5 5 90 11.8 2.0 9.20 2.712621 1.190 20.770 7.73 104.20
T7 2 2 15 5 4 90 11.2 2.5 10.00 2.931980 1.240 19.600 8.06 105.00
T7 2 2 15 5 5 80 12.2 2.0 9.00 2.657782 1.250 19.600 7.20 105.00
T7 2 2 15 5 1 70 10.8 2.5 9.80 2.877140 1.240 19.600 7.90 105.00
T7 2 2 15 5 3 50 13.2 2.0 8.60 2.548103 1.310 19.600 6.56 105.00
T7 2 2 15 5 2 60 12.4 2.0 8.80 2.602942 1.220 19.600 7.21 105.00
T7 2 3 15 5 5 50 13.4 2.0 9.00 2.657782 1.350 19.596 6.67 104.40
T7 2 3 15 5 2 50 10.8 2.5 9.50 2.794881 1.240 19.596 7.66 104.40
T7 2 3 15 5 4 60 12.0 2.0 9.00 2.657782 1.290 19.596 6.98 104.40
T7 2 3 15 5 1 80 12.0 2.0 9.60 2.822300 1.260 19.596 7.62 104.40
T7 2 3 15 5 3 90 12.0 2.0 9.00 2.657782 1.280 19.596 7.03 104.40
T7 3 1 15 5 3 90 12.8 3.0 10.25 2.260000 1.250 20.802 8.20 107.20
T7 3 1 15 5 1 80 12.9 2.0 9.65 2.836010 1.180 20.802 8.18 107.20
T7 3 1 15 5 2 90 12.8 3.0 9.45 2.781171 1.190 20.802 7.94 107.20
T7 3 1 15 5 4 80 12.9 2.0 8.85 2.616652 1.240 20.802 7.14 107.20
T7 3 1 15 5 5 50 12.0 2.0 9.10 2.685201 1.260 20.802 7.22 107.20
T7 3 2 15 5 3 80 13.0 2.0 8.45 2.506973 1.340 20.752 6.31 108.40
T7 3 2 15 5 1 70 12.8 2.0 9.65 2.340000 1.220 20.752 7.91 108.40
T7 3 2 15 5 2 60 11.0 2.0 8.65 2.561812 1.230 20.752 7.03 108.40
T7 3 2 15 5 4 90 13.4 2.5 9.85 2.890850 1.335 20.752 7.38 108.40
T7 3 2 15 5 5 80 13.0 2.0 9.10 2.685201 1.360 20.752 6.69 108.40
T7 3 3 15 5 4 80 12.0 2.0 9.00 2.657782 1.300 19.976 6.92 109.40
T7 3 3 15 5 5 60 12.0 2.0 9.85 2.890850 1.200 19.976 8.21 109.40
T7 3 3 15 5 1 90 11.4 2.5 9.45 2.781171 1.390 19.976 6.80 109.40
T7 3 3 15 5 2 50 12.9 2.0 9.35 2.753751 1.250 19.976 7.48 109.40
T7 3 3 15 5 3 80 12.6 2.0 9.25 2.726331 1.220 19.976 7.58 109.40
T8 1 1 15 10 3 90 13.9 2.5 9.00 2.657782 1.200 21.564 7.50 119.48
T8 1 1 15 10 4 80 13.3 2.0 9.40 2.767461 1.230 21.564 7.64 119.48
T8 1 1 15 10 2 90 13.8 2.5 9.40 2.657782 1.240 21.564 7.58 119.48
T8 1 1 15 10 5 50 13.8 2.5 8.50 2.602942 1.140 21.564 7.46 119.48
T8 1 1 15 10 1 60 10.8 2.5 9.20 3.069079 1.280 21.564 7.19 119.48
T8 1 2 15 10 3 50 12.6 2.5 9.50 2.986819 1.320 21.324 7.20 118.30
T8 1 2 15 10 1 60 13.2 2.5 8.30 2.465843 1.130 21.324 7.35 118.30
T8 1 2 15 10 4 90 11.6 2.0 9.90 2.904560 1.190 21.324 8.32 118.30
T8 1 2 15 10 5 90 14.0 2.5 9.00 2.657782 1.180 21.324 7.63 118.30
T8 1 2 15 10 2 90 13.5 2.5 9.90 2.904560 1.310 21.324 7.56 118.30
T8 1 3 15 10 1 80 13.4 2.5 8.00 2.383584 1.220 21.720 6.56 120.10
T8 1 3 15 10 5 90 12.6 2.5 8.20 2.383584 1.280 21.720 6.41 120.10
T8 1 3 15 10 4 80 13.6 2.5 8.00 2.383584 1.260 21.720 6.35 120.10
T8 1 3 15 10 2 70 13.0 2.0 9.00 2.657782 1.240 21.720 7.26 120.10
T8 1 3 15 10 3 90 13.4 2.0 8.20 2.383584 1.250 21.720 6.56 120.10
T8 2 1 15 10 2 90 12.8 2.5 9.80 2.877140 1.150 20.886 8.52 126.48
T8 2 1 15 10 1 70 12.8 2.5 10.50 3.069079 1.220 20.886 8.61 126.48
T8 2 1 15 10 3 90 11.8 2.5 9.70 2.849720 1.160 20.886 8.36 126.48
T8 2 1 15 10 4 90 13.3 3.0 9.40 2.767461 1.140 20.886 8.25 126.48
T8 2 1 15 10 5 70 12.9 2.5 8.80 2.602942 1.180 20.886 7.46 126.48
T8 2 2 15 10 1 50 13.2 2.5 10.30 3.014239 1.290 21.220 7.98 128.20
T8 2 2 15 10 2 90 12.4 2.5 9.00 2.657782 1.240 21.220 7.26 128.20
T8 2 2 15 10 3 80 12.4 2.5 10.20 2.986819 1.220 21.220 8.36 128.20
T8 2 2 15 10 4 60 12.6 2.5 9.30 2.740041 1.230 21.220 7.56 128.20
T8 2 2 15 10 5 80 14.0 2.5 9.00 2.657782 1.150 21.220 7.83 128.20
T8 2 3 15 10 5 90 12.9 2.5 8.00 2.383584 1.120 21.394 7.14 126.80
T8 2 3 15 10 2 80 13.0 2.0 8.20 2.438423 1.140 21.394 7.19 126.80
T8 2 3 15 10 3 90 13.4 2.0 8.00 2.383584 1.130 21.394 7.08 126.80
T8 2 3 15 10 4 70 13.6 2.5 9.00 2.657782 1.110 21.394 8.11 126.80
T8 2 3 15 10 1 80 12.2 2.5 12.10 3.507795 1.160 21.394 10.43 126.80
T8 3 1 15 10 1 70 12.0 3.0 9.90 3.027949 1.380 19.600 7.17 94.20
T8 3 1 15 10 2 80 12.2 2.5 10.20 2.836010 1.170 19.600 8.72 94.20
T8 3 1 15 10 3 70 12.8 3.0 9.70 2.808591 1.180 19.600 8.22 94.20
T8 3 1 15 10 4 80 13.5 2.5 9.25 2.830000 1.350 19.600 6.85 94.20
T8 3 1 15 10 5 90 10.8 3.0 9.80 2.920000 1.280 19.600 7.66 94.20
T8 3 2 15 10 1 90 13.4 2.5 9.70 2.973109 1.390 20.804 6.98 94.20
T8 3 2 15 10 2 80 12.9 2.5 9.80 2.940000 1.180 20.804 8.31 94.20
T8 3 2 15 10 3 70 12.9 3.0 9.60 2.580000 1.250 20.804 7.68 94.20
T8 3 2 15 10 4 70 12.9 2.0 9.80 3.010000 1.160 20.804 8.45 94.20
T8 3 2 15 10 5 60 14.2 2.5 9.80 2.860000 1.350 20.804 7.26 94.20
T8 3 3 15 10 1 90 12.4 2.5 9.00 2.880000 1.360 20.140 6.62 94.30
T8 3 3 15 10 2 80 13.2 2.0 8.70 2.630362 1.340 20.140 6.49 94.30
T8 3 3 15 10 3 90 13.6 3.0 10.20 2.880000 1.380 20.140 7.39 94.30
T8 3 3 15 10 4 90 13.8 2.5 9.20 2.726331 1.340 20.140 6.87 94.30
T8 3 3 15 10 5 90 12.8 3.0 9.40 2.950000 1.360 20.140 6.91 94.30

consumo %>% kable(caption = "Evaluation of fruits at commercial maturity")
Evaluation of fruits at commercial maturity
treat composts biol repetition n_fruits pdfmc ffmc cifmc ssfmc phfmc atfmc imf
T0 0 0 1 1 6.68 3.0 3.0 14.6 4.22 0.600 24.33333
T0 0 0 1 2 6.78 3.0 3.0 15.6 4.19 0.700 22.28571
T0 0 0 1 3 7.02 4.0 3.0 14.8 4.21 0.700 21.14286
T0 0 0 1 4 6.78 3.8 3.0 15.0 4.15 0.500 30.00000
T0 0 0 1 5 6.12 4.2 3.0 14.0 4.26 0.500 28.00000
T0 0 0 2 1 6.62 4.0 3.0 14.4 4.34 0.605 23.80165
T0 0 0 2 2 6.91 3.6 3.5 15.5 4.35 0.615 25.20325
T0 0 0 2 3 7.04 3.0 3.0 15.1 4.34 0.625 24.16000
T0 0 0 2 4 6.45 3.0 3.0 15.2 4.30 0.600 25.33333
T0 0 0 2 5 6.50 3.0 3.0 15.2 4.38 0.600 25.33333
T0 0 0 3 1 6.55 3.0 3.5 15.0 4.45 0.595 25.21008
T0 0 0 3 2 6.85 3.4 3.0 15.0 4.46 0.590 25.42373
T0 0 0 3 3 7.12 4.2 3.0 14.5 4.50 0.590 24.57627
T0 0 0 3 4 6.95 3.8 3.5 15.0 4.42 0.600 25.00000
T0 0 0 3 5 7.04 4.2 2.5 15.0 4.41 0.600 25.00000
T1 5 0 1 1 6.62 4.2 3.5 14.0 4.10 0.710 19.71831
T1 5 0 1 2 6.70 3.8 3.0 14.0 4.00 0.720 19.44444
T1 5 0 1 3 6.47 3.8 4.0 14.0 4.20 0.740 18.91892
T1 5 0 1 4 6.53 3.8 3.5 15.6 4.30 0.690 22.60870
T1 5 0 1 5 6.60 3.6 4.0 15.0 3.90 0.710 21.12676
T1 5 0 2 1 6.45 3.8 3.0 14.0 4.42 0.620 22.58065
T1 5 0 2 2 6.87 3.6 3.0 14.0 4.45 0.600 23.33333
T1 5 0 2 3 6.48 3.4 3.0 15.6 4.41 0.640 24.37500
T1 5 0 2 4 6.70 4.0 3.5 15.5 4.43 0.630 24.60317
T1 5 0 2 5 6.35 4.2 3.5 15.0 4.41 0.620 24.19355
T1 5 0 3 1 6.95 4.0 3.5 14.8 4.00 0.520 28.46154
T1 5 0 3 2 6.62 4.4 4.0 16.2 3.80 0.500 32.40000
T1 5 0 3 3 6.70 3.8 4.0 14.6 3.90 0.530 27.54717
T1 5 0 3 4 7.04 3.0 4.2 16.4 4.20 0.540 30.37037
T1 5 0 3 5 7.20 3.8 4.2 14.0 4.10 0.520 26.92308
T2 15 0 1 1 6.07 4.8 3.5 16.0 4.55 0.520 30.76923
T2 15 0 1 2 6.20 4.0 4.0 15.8 4.50 0.510 30.98039
T2 15 0 1 3 6.28 4.2 3.0 15.8 4.56 0.520 30.38462
T2 15 0 1 4 6.37 3.8 3.0 15.8 4.54 0.530 29.81132
T2 15 0 1 5 6.70 3.8 4.0 14.6 4.58 0.540 27.03704
T2 15 0 2 1 5.95 3.8 4.5 16.2 4.50 0.515 31.45631
T2 15 0 2 2 6.20 4.0 3.5 15.0 4.44 0.510 29.41176
T2 15 0 2 3 6.11 4.0 3.0 15.2 4.48 0.535 28.41121
T2 15 0 2 4 6.28 3.8 4.5 15.8 4.56 0.505 31.28713
T2 15 0 2 5 6.87 4.8 4.0 16.0 4.50 0.515 31.06796
T2 15 0 3 1 6.20 4.2 3.5 14.8 4.52 0.520 28.46154
T2 15 0 3 2 6.28 4.0 4.0 15.4 4.45 0.525 29.33333
T2 15 0 3 3 6.70 4.0 4.0 15.8 4.57 0.510 30.98039
T2 15 0 3 4 6.20 4.2 3.0 16.0 4.49 0.515 31.06796
T2 15 0 3 5 6.11 4.4 3.5 16.0 4.56 0.520 30.76923
T3 0 5 1 1 6.70 3.2 3.5 14.5 4.20 0.585 24.78632
T3 0 5 1 2 6.62 3.4 4.0 14.8 4.15 0.595 24.87395
T3 0 5 1 3 6.62 3.0 3.5 15.0 4.25 0.595 25.21008
T3 0 5 1 4 6.62 3.8 3.5 15.2 4.18 0.585 25.98291
T3 0 5 1 5 6.11 4.2 3.0 16.0 4.22 0.590 27.11864
T3 0 5 2 1 6.78 4.4 3.0 14.2 4.51 0.595 23.86555
T3 0 5 2 2 6.28 3.2 4.0 14.8 4.48 0.605 24.46281
T3 0 5 2 3 6.37 3.0 3.5 14.6 4.46 0.595 24.53782
T3 0 5 2 4 6.62 3.0 3.5 15.0 4.58 0.595 25.21008
T3 0 5 2 5 6.70 3.0 3.5 16.4 4.54 0.605 27.10744
T3 0 5 3 1 6.20 3.0 4.0 14.8 4.39 0.590 25.08475
T3 0 5 3 2 6.45 3.6 3.5 15.0 4.40 0.595 25.21008
T3 0 5 3 3 6.62 3.0 3.5 16.0 4.36 0.585 27.35043
T3 0 5 3 4 6.70 4.0 4.0 14.8 4.45 0.615 24.06504
T3 0 5 3 5 6.70 3.8 3.5 14.6 4.37 0.580 25.17241
T4 0 10 1 1 6.37 4.2 4.0 15.2 4.38 0.550 27.63636
T4 0 10 1 2 6.45 4.2 4.0 15.4 4.36 0.620 24.83871
T4 0 10 1 3 6.45 4.0 3.5 15.4 4.35 0.615 25.04065
T4 0 10 1 4 6.40 4.0 4.0 15.4 4.42 0.625 24.64000
T4 0 10 1 5 6.41 4.0 4.0 15.3 4.40 0.555 27.56757
T4 0 10 2 1 6.45 4.0 4.0 15.4 4.39 0.545 28.25688
T4 0 10 2 2 6.41 3.8 3.5 16.3 4.41 0.615 26.50407
T4 0 10 2 3 6.62 3.6 4.0 15.8 4.42 0.625 25.28000
T4 0 10 2 4 6.20 4.2 3.5 14.8 4.37 0.605 24.46281
T4 0 10 2 5 6.32 4.8 4.0 15.1 4.38 0.615 24.55285
T4 0 10 3 1 6.24 4.2 4.0 14.9 4.39 0.580 25.68966
T4 0 10 3 2 6.11 4.0 2.5 15.6 4.43 0.595 26.21849
T4 0 10 3 3 6.62 4.2 3.5 15.8 4.35 0.570 27.71930
T4 0 10 3 4 6.62 4.0 4.0 15.8 4.36 0.600 26.33333
T4 0 10 3 5 6.45 3.6 4.0 15.4 4.41 0.605 25.45455
T5 5 5 1 1 6.53 4.0 4.0 14.8 4.25 0.590 25.08475
T5 5 5 1 2 6.62 4.2 4.0 16.0 4.55 0.545 29.35780
T5 5 5 1 3 6.45 4.0 3.0 14.3 4.60 0.535 26.72897
T5 5 5 1 4 6.53 4.0 3.5 15.0 4.44 0.555 27.02703
T5 5 5 1 5 6.28 3.8 3.5 16.0 4.26 0.540 29.62963
T5 5 5 2 1 6.70 4.0 3.5 14.9 4.41 0.595 25.04202
T5 5 5 2 2 6.70 4.0 3.5 16.0 4.45 0.535 29.90654
T5 5 5 2 3 6.45 4.0 3.5 15.2 4.38 0.545 27.88991
T5 5 5 2 4 6.45 4.0 3.5 15.3 4.46 0.555 27.56757
T5 5 5 2 5 6.20 4.2 4.0 14.8 4.45 0.540 27.40741
T5 5 5 3 1 6.41 4.0 4.0 15.4 4.39 0.545 28.25688
T5 5 5 3 2 6.37 4.0 4.0 15.2 4.45 0.555 27.38739
T5 5 5 3 3 6.62 4.0 3.5 15.2 4.39 0.535 28.41121
T5 5 5 3 4 6.70 4.2 3.5 15.2 4.47 0.540 28.14815
T5 5 5 3 5 6.37 4.2 3.5 15.0 4.49 0.535 28.03738
T6 5 10 1 1 6.20 4.8 3.5 15.6 4.48 0.535 29.15888
T6 5 10 1 2 6.70 4.0 4.0 15.8 4.46 0.525 30.09524
T6 5 10 1 3 5.99 4.2 3.5 16.4 4.51 0.545 30.09174
T6 5 10 1 4 6.28 3.8 3.5 15.6 4.52 0.525 29.71429
T6 5 10 1 5 6.70 3.6 3.5 15.0 4.45 0.525 28.57143
T6 5 10 2 1 6.24 3.8 3.0 16.0 4.42 0.535 29.90654
T6 5 10 2 2 6.70 4.0 4.0 16.0 4.25 0.565 28.31858
T6 5 10 2 3 6.37 4.0 3.5 15.4 4.21 0.535 28.78505
T6 5 10 2 4 6.41 3.8 4.0 15.4 4.28 0.555 27.74775
T6 5 10 2 5 6.20 4.8 3.5 14.8 4.29 0.525 28.19048
T6 5 10 3 1 6.45 4.2 4.0 14.4 4.68 0.530 27.16981
T6 5 10 3 2 6.37 4.0 3.5 15.2 4.75 0.535 28.41121
T6 5 10 3 3 6.37 4.0 3.5 15.2 4.61 0.525 28.95238
T6 5 10 3 4 6.37 4.2 4.0 16.0 4.73 0.540 29.62963
T6 5 10 3 5 6.28 4.2 3.5 15.2 4.62 0.520 29.23077
T7 15 5 1 1 6.11 4.8 3.5 16.2 4.57 0.515 31.45631
T7 15 5 1 2 6.53 4.0 3.0 16.2 4.51 0.520 31.15385
T7 15 5 1 3 6.20 4.0 3.0 16.8 4.52 0.510 32.94118
T7 15 5 1 4 6.28 4.2 4.0 14.8 4.59 0.515 28.73786
T7 15 5 1 5 5.86 4.0 3.5 14.6 4.64 0.520 28.07692
T7 15 5 2 1 6.03 4.2 3.5 15.8 4.56 0.510 30.98039
T7 15 5 2 2 6.28 3.8 4.0 15.9 4.54 0.515 30.87379
T7 15 5 2 3 6.32 4.2 4.0 16.8 4.58 0.505 33.26733
T7 15 5 2 4 6.37 4.2 4.0 15.4 4.49 0.505 30.49505
T7 15 5 2 5 6.37 4.8 4.0 14.6 4.62 0.505 28.91089
T7 15 5 3 1 6.28 4.4 3.5 15.2 4.58 0.520 29.23077
T7 15 5 3 2 6.28 4.4 4.0 15.2 4.57 0.528 28.78788
T7 15 5 3 3 6.07 4.6 4.0 16.2 4.56 0.505 32.07921
T7 15 5 3 4 6.28 3.6 3.5 15.4 4.61 0.515 29.90291
T7 15 5 3 5 6.28 4.0 4.0 16.8 4.59 0.520 32.30769
T8 15 10 1 1 5.86 4.0 4.0 15.8 4.60 0.520 30.38462
T8 15 10 1 2 5.86 4.0 4.0 16.0 4.52 0.530 30.18868
T8 15 10 1 3 5.86 4.0 4.0 16.4 4.58 0.540 30.37037
T8 15 10 1 4 6.13 4.2 3.5 15.6 4.63 0.520 30.00000
T8 15 10 1 5 6.18 4.6 3.0 16.0 4.65 0.500 32.00000
T8 15 10 2 1 5.86 4.4 4.0 15.4 4.67 0.510 30.19608
T8 15 10 2 2 5.96 5.2 4.0 16.4 4.71 0.470 34.89362
T8 15 10 2 3 6.53 4.0 3.5 16.2 4.65 0.490 33.06122
T8 15 10 2 4 6.39 4.2 4.0 16.0 4.63 0.500 32.00000
T8 15 10 2 5 6.32 4.0 3.5 16.0 4.69 0.490 32.65306
T8 15 10 3 1 6.20 4.4 4.0 16.6 4.81 0.410 40.48780
T8 15 10 3 2 6.78 4.2 4.0 15.5 4.72 0.400 38.75000
T8 15 10 3 3 5.95 4.4 4.0 16.0 4.87 0.420 38.09524
T8 15 10 3 4 6.70 4.8 3.5 16.7 4.85 0.390 42.82051
T8 15 10 3 5 5.86 4.6 4.0 17.2 4.81 0.430 40.00000

3 Data summary

Summary of the number of data points recorded for each treatment and evaluated variable.

sm <- fisio %>% 
  group_by(treat) %>% 
  summarise(across(pcfmf:rpp, ~ sum(!is.na(.))))

sm
## # A tibble: 9 × 10
##   treat pcfmf  ffmf cifmf ssfmf phfmf atfmf msfmf   imf   rpp
##   <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 T0       45    45    45    45    45    45    45    45    45
## 2 T1       45    45    45    45    45    45    45    45    45
## 3 T2       45    45    45    45    45    45    45    45    45
## 4 T3       45    45    45    45    45    45    45    45    45
## 5 T4       45    45    45    45    45    45    45    45    45
## 6 T5       45    45    45    45    45    45    45    45    45
## 7 T6       45    45    45    45    45    45    45    45    45
## 8 T7       45    45    45    45    45    45    45    45    45
## 9 T8       45    45    45    45    45    45    45    45    45

sm <- consumo %>% 
  group_by(treat) %>% 
  summarise(across(pdfmc:imf, ~ sum(!is.na(.))))

sm
## # A tibble: 9 × 8
##   treat pdfmc  ffmc cifmc ssfmc phfmc atfmc   imf
##   <fct> <int> <int> <int> <int> <int> <int> <int>
## 1 T0       15    15    15    15    15    15    15
## 2 T1       15    15    15    15    15    15    15
## 3 T2       15    15    15    15    15    15    15
## 4 T3       15    15    15    15    15    15    15
## 5 T4       15    15    15    15    15    15    15
## 6 T5       15    15    15    15    15    15    15
## 7 T6       15    15    15    15    15    15    15
## 8 T7       15    15    15    15    15    15    15
## 9 T8       15    15    15    15    15    15    15

4 Meteorological data

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 3

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8) + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8) +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8) +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 40)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top")

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.jpg", units = "cm"
         , width = 25, height = 15)

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.eps", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/Figure_2.jpg")

5 Objetives

The objective of this study is to demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at physiological and commercial maturity.

5.1 Specific Objective 1

Demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at physiological maturity.

5.1.1 Fruit firmness at physiological maturity (FF_pm)

trait <- "ffmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        ffmf        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ffmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   5343.0467  1  12.86 < 0.00000000000000022 ***
## composts         5.5087  2 394.00              0.004369 ** 
## biol            10.4587  2 394.00            0.00003753 ***
## composts:biol    0.5815  4 394.00              0.676230    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 11.38667 0.1557766 12.86214 11.04976 11.72357 bB
0 15 11.95556 0.1557766 12.86214 11.61865 12.29246 bA
0 5 11.49333 0.1557766 12.86214 11.15643 11.83024 bB
10 0 12.22000 0.1557766 12.86214 11.88310 12.55690 aB
10 15 12.96000 0.1557766 12.86214 12.62310 13.29690 aA
10 5 12.20444 0.1557766 12.86214 11.86754 12.54135 aB
5 0 11.80444 0.1557766 12.86214 11.46754 12.14135 abB
5 15 12.33111 0.1557766 12.86214 11.99421 12.66801 bA
5 5 11.94667 0.1557766 12.86214 11.60976 12.28357 aAB

p1a <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at physiological maturity (kgf/cm^{2})"
           , glab = "Biol"
           # , ylimits = c(0, 14, 4)
           , type = "line"
           )

p1a


mc_1a <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_a <- mc_1a %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at physiological maturity (kgf/cm^{2})"
           , glab = "Biol"
           , ylimits = c(0, 15, 3)
           , type = "bar"
           )


# # Calcular el valor máximo (upper whisker) por grupo
# positions <-  rmout$data$clean %>%
#   group_by(composts, biol) %>%
#   summarise(y_pos = max(ffmf, na.rm = TRUE) + 0.3, .groups = "drop")
# 
# # Combinar con las letras del objeto mc
# mc_pos <- left_join(mc, positions, by = c("composts", "biol"))
# 
# # Gráfico
# p1a <- ggplot( rmout$data$clean, aes(x = factor(composts), y = ffmf, fill = factor(biol))) +
#   stat_boxplot(geom = "errorbar", width = 0.5, position = position_dodge(0.8)) +
#   geom_boxplot(width = 0.5, position = position_dodge(0.8)) +
#   geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
#               shape = 21, size = 1.8, alpha = 0.6, color = "black") +
#   geom_text(data = mc_pos,
#             aes(x = factor(composts), y = y_pos, label = group, fill = factor(biol)),
#             position = position_dodge(width = 0.8),
#             size = 4,
#             inherit.aes = FALSE) +
#   labs(x = "Composts", y = "Fruit firmness at physiological maturity (kgf/cm²)", fill = "Biol") +
#   theme_minimal() +
#   theme(legend.position = "top") +
#     theme(
#     panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.5),
#     panel.background = element_blank()
#   )

5.1.2 Percentage of fruit canopy cover at physiological maturity (PFCC_pm).

trait <- "pcfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        pcfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: pcfmf
##                      F Df Df.res                Pr(>F)    
## (Intercept)   472.1730  1 122.28 < 0.00000000000000022 ***
## composts        5.7549  2 394.00             0.0034393 ** 
## biol            7.5822  2 394.00             0.0005873 ***
## composts:biol   0.2692  4 394.00             0.8977598    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 56.44444 2.59759 122.2759 51.30237 61.58652 bB
0 15 68.66667 2.59759 122.2759 63.52459 73.80874 bA
0 5 60.44444 2.59759 122.2759 55.30237 65.58652 bAB
10 0 70.22222 2.59759 122.2759 65.08015 75.36430 aA
10 15 78.66667 2.59759 122.2759 73.52459 83.80874 aA
10 5 73.11111 2.59759 122.2759 67.96904 78.25318 aA
5 0 66.66667 2.59759 122.2759 61.52459 71.80874 aA
5 15 73.77778 2.59759 122.2759 68.63570 78.91985 abA
5 5 68.22222 2.59759 122.2759 63.08015 73.36430 abA

p1b <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Percentage of fruit canopy cover at physiological maturity ('%')"
           # , ylimits = c(0, 3, 1)
           , glab = "Biol"
           , type = "line"
           ) 

p1b


mc_1b <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_b <- mc_1b %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Percentage of fruit canopy cover at physiological maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 90, 15)
           , type = "bar"
           )

5.1.3 Fruit pulp color at physiological maturity (FPC_pm)

trait <- "cifmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##     index repetition composts biol cifmf       resi    res_MAD
## 6       6          1        0    0   1.5 -0.6599549 -13.354004
## 9       9          1        0    0   2.5  0.3400451   6.880719
## 10     10          1        0    0   2.5  0.3400451   6.880719
## 11     11          1        0    0   2.5  0.3400451   6.880719
## 18     18          2        0    0   2.5  0.3400451   6.880719
## 21     21          2        0    0   2.5  0.3400451   6.880719
## 23     23          2        0    0   2.5  0.3400451   6.880719
## 24     24          2        0    0   2.5  0.3400451   6.880719
## 25     25          2        0    0   2.5  0.3400451   6.880719
## 26     26          2        0    0   2.5  0.3400451   6.880719
## 27     27          2        0    0   2.5  0.3400451   6.880719
## 28     28          2        0    0   2.5  0.3400451   6.880719
## 34     34          3        0    0   1.5 -0.6800902 -13.761438
## 36     36          3        0    0   3.0  0.8199098  16.590647
## 37     37          3        0    0   2.5  0.3199098   6.473285
## 38     38          3        0    0   2.5  0.3199098   6.473285
## 40     40          3        0    0   3.0  0.8199098  16.590647
## 52     52          1        5    0   2.5  0.3733785   7.555210
## 54     54          1        5    0   2.5  0.3733785   7.555210
## 55     55          1        5    0   2.5  0.3733785   7.555210
## 67     67          2        5    0   1.5 -0.6266215 -12.679513
## 68     68          2        5    0   2.5  0.3733785   7.555210
## 69     69          2        5    0   1.0 -1.1266215 -22.796875
## 70     70          2        5    0   2.5  0.3733785   7.555210
## 72     72          2        5    0   2.5  0.3733785   7.555210
## 76     76          3        5    0   2.5  0.3532431   7.147776
## 81     81          3        5    0   2.5  0.3532431   7.147776
## 83     83          3        5    0   2.5  0.3532431   7.147776
## 84     84          3        5    0   2.5  0.3532431   7.147776
## 85     85          3        5    0   2.5  0.3532431   7.147776
## 86     86          3        5    0   2.5  0.3532431   7.147776
## 87     87          3        5    0   2.5  0.3532431   7.147776
## 89     89          3        5    0   2.5  0.3532431   7.147776
## 90     90          3        5    0   2.5  0.3532431   7.147776
## 94     94          1       15    0   2.5  0.3733785   7.555210
## 96     96          1       15    0   2.5  0.3733785   7.555210
## 103   103          1       15    0   2.5  0.3733785   7.555210
## 104   104          1       15    0   2.5  0.3733785   7.555210
## 105   105          1       15    0   2.5  0.3733785   7.555210
## 108   108          2       15    0   2.5  0.3733785   7.555210
## 111   111          2       15    0   1.5 -0.6266215 -12.679513
## 116   116          2       15    0   2.5  0.3733785   7.555210
## 119   119          2       15    0   2.5  0.3733785   7.555210
## 124   124          3       15    0   2.5  0.3532431   7.147776
## 126   126          3       15    0   2.5  0.3532431   7.147776
## 129   129          3       15    0   2.5  0.3532431   7.147776
## 134   134          3       15    0   2.5  0.3532431   7.147776
## 135   135          3       15    0   2.5  0.3532431   7.147776
## 141   141          1        0    5   2.5  0.3400451   6.880719
## 142   142          1        0    5   2.5  0.3400451   6.880719
## 144   144          1        0    5   2.5  0.3400451   6.880719
## 146   146          1        0    5   2.5  0.3400451   6.880719
## 147   147          1        0    5   2.5  0.3400451   6.880719
## 156   156          2        0    5   2.5  0.3400451   6.880719
## 158   158          2        0    5   2.5  0.3400451   6.880719
## 160   160          2        0    5   2.5  0.3400451   6.880719
## 162   162          2        0    5   2.5  0.3400451   6.880719
## 163   163          2        0    5   2.5  0.3400451   6.880719
## 171   171          3        0    5   3.0  0.8199098  16.590647
## 175   175          3        0    5   2.5  0.3199098   6.473285
## 176   176          3        0    5   2.5  0.3199098   6.473285
## 177   177          3        0    5   2.5  0.3199098   6.473285
## 183   183          1        0   10   2.5  0.3733785   7.555210
## 185   185          1        0   10   2.5  0.3733785   7.555210
## 186   186          1        0   10   2.5  0.3733785   7.555210
## 189   189          1        0   10   2.5  0.3733785   7.555210
## 191   191          1        0   10   2.5  0.3733785   7.555210
## 192   192          1        0   10   2.5  0.3733785   7.555210
## 195   195          1        0   10   2.5  0.3733785   7.555210
## 204   204          2        0   10   2.5  0.3733785   7.555210
## 206   206          2        0   10   2.5  0.3733785   7.555210
## 208   208          2        0   10   1.5 -0.6266215 -12.679513
## 213   213          3        0   10   2.5  0.3532431   7.147776
## 223   223          3        0   10   3.0  0.8532431  17.265137
## 225   225          3        0   10   2.5  0.3532431   7.147776
## 231   231          1        5    5   2.5  0.3733785   7.555210
## 234   234          1        5    5   2.5  0.3733785   7.555210
## 235   235          1        5    5   2.5  0.3733785   7.555210
## 236   236          1        5    5   2.5  0.3733785   7.555210
## 243   243          2        5    5   2.5  0.3733785   7.555210
## 244   244          2        5    5   2.5  0.3733785   7.555210
## 246   246          2        5    5   2.5  0.3733785   7.555210
## 248   248          2        5    5   2.5  0.3733785   7.555210
## 250   250          2        5    5   2.5  0.3733785   7.555210
## 253   253          2        5    5   2.5  0.3733785   7.555210
## 255   255          2        5    5   2.5  0.3733785   7.555210
## 257   257          3        5    5   2.5  0.3532431   7.147776
## 269   269          3        5    5   1.5 -0.6467569 -13.086947
## 270   270          3        5    5   2.5  0.3532431   7.147776
## 281   281          1        5   10   2.5  0.3956007   8.004870
## 282   282          1        5   10   2.5  0.3956007   8.004870
## 285   285          1        5   10   2.5  0.3956007   8.004870
## 296   296          2        5   10   2.5  0.3956007   8.004870
## 298   298          2        5   10   2.5  0.3956007   8.004870
## 300   300          2        5   10   2.5  0.3956007   8.004870
## 301   301          3        5   10   2.5  0.3754653   7.597437
## 305   305          3        5   10   2.5  0.3754653   7.597437
## 310   310          3        5   10   2.5  0.3754653   7.597437
## 311   311          3        5   10   2.5  0.3754653   7.597437
## 316   316          1       15    5   2.5  0.3511562   7.105549
## 321   321          1       15    5   2.5  0.3511562   7.105549
## 325   325          1       15    5   2.5  0.3511562   7.105549
## 329   329          1       15    5   2.5  0.3511562   7.105549
## 332   332          2       15    5   2.5  0.3511562   7.105549
## 336   336          2       15    5   2.5  0.3511562   7.105549
## 338   338          2       15    5   2.5  0.3511562   7.105549
## 342   342          2       15    5   2.5  0.3511562   7.105549
## 346   346          3       15    5   3.0  0.8310209  16.815477
## 348   348          3       15    5   3.0  0.8310209  16.815477
## 354   354          3       15    5   2.5  0.3310209   6.698116
## 358   358          3       15    5   2.5  0.3310209   6.698116
## 362   362          1       15   10   2.0 -0.4821771  -9.756720
## 368   368          1       15   10   2.0 -0.4821771  -9.756720
## 374   374          1       15   10   2.0 -0.4821771  -9.756720
## 375   375          1       15   10   2.0 -0.4821771  -9.756720
## 379   379          2       15   10   3.0  0.5178229  10.478003
## 387   387          2       15   10   2.0 -0.4821771  -9.756720
## 388   388          2       15   10   2.0 -0.4821771  -9.756720
## 391   391          3       15   10   3.0  0.4976875  10.070569
## 393   393          3       15   10   3.0  0.4976875  10.070569
## 395   395          3       15   10   3.0  0.4976875  10.070569
## 398   398          3       15   10   3.0  0.4976875  10.070569
## 399   399          3       15   10   2.0 -0.5023125 -10.164153
## 402   402          3       15   10   2.0 -0.5023125 -10.164153
## 403   403          3       15   10   3.0  0.4976875  10.070569
## 405   405          3       15   10   3.0  0.4976875  10.070569
##                 rawp.BHStud                    adjp                 bholm
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model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: cifmf
##                                                    F Df  Df.res
## (Intercept)   388546674060826958166444280448482.0000  1  68.829
## composts                                      5.7018  2 269.069
## biol                                         18.3463  2 268.807
## composts:biol   3172041012006057928644084004806.0000  4 268.757
##                              Pr(>F)    
## (Intercept)   < 0.00000000000000022 ***
## composts                   0.003757 ** 
## biol                  0.00000003398 ***
## composts:biol < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 2.0 0 68.82891 2.0 2.0 aA
0 15 2.0 0 60.12061 2.0 2.0 cB
0 5 2.0 0 67.35437 2.0 2.0 aA
10 0 2.0 0 56.31237 2.0 2.0 aB
10 15 2.5 0 61.57101 2.5 2.5 aA
10 5 2.0 0 49.80070 2.0 2.0 aB
5 0 2.0 0 60.11941 2.0 2.0 bB
5 15 2.0 0 54.66689 2.0 2.0 bA
5 5 2.0 0 59.25250 2.0 2.0 aA

p1c <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pulp color at physiological maturity"
           # , ylimits = c(0, 3, 1)
           , glab = "Biol"
           , type = "line"
           ) 
  # geom_text_repel(data = mc,
  #               aes(x = composts, y = emmean, label = group, group = biol),
  #               size = 3.5,
  #               max.overlaps = Inf, # para mostrar todas las letras
  #               box.padding = 0.3,
  #               point.padding = 0.2,
  #               segment.color = "grey50",
  #               segment.size = 0.2,
  #               colour = "black") +
  # geom_text(color = )

p1c


mc_1c <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_c <- mc_1c %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pulp color at physiological maturity"
           , glab = "Biol"
           , ylimits = c(0, 3, 1)
           , type = "bar"
           )

5.1.4 Fruit pH at physiological maturity (FpH_pm)

trait <- "phfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##     index repetition composts biol    phfmf      resi res_MAD  rawp.BHStud
## 390   390          2       15   10 3.507795 0.7353291 3.98034 0.0000688167
##             adjp      bholm out_flag
## 390 0.0000688167 0.02787076  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: phfmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   7948.5863  1  35.09 < 0.00000000000000022 ***
## composts         8.1192  2 393.00             0.0003506 ***
## biol            11.8798  2 393.00           0.000009786 ***
## composts:biol    3.9222  4 393.00             0.0039002 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 2.594716 0.0291035 35.08913 2.535638 2.653794 bB
0 15 2.692818 0.0291035 35.08913 2.633740 2.751896 aA
0 5 2.540791 0.0291035 35.08913 2.481713 2.599868 cB
10 0 2.711098 0.0291035 35.08913 2.652020 2.770176 aA
10 15 2.745743 0.0293898 36.37317 2.686158 2.805327 aA
10 5 2.737604 0.0291035 35.08913 2.678526 2.796682 aA
5 0 2.526739 0.0291035 35.08913 2.467661 2.585816 bB
5 15 2.710735 0.0291035 35.08913 2.651657 2.769812 aA
5 5 2.634018 0.0291035 35.08913 2.574940 2.693096 bA

# p1c <- mc %>% 
#   plot_smr(x = "composts"
#            , y = "emmean"
#            , group = "biol"
#            , sig = "group"
#            , error = "SE"
#            , color = T
#            , xlab = "Composts"
#            , ylab = "Fruit pH at physiological maturity"
#            # , ylimits = c(0, 4, 1)
#            , type = "line"
#            )
# 
# p1d

5.1.5 Soluble solids content of the fruit at physiological maturity (SSCF_pm)

trait <- "ssfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##     index repetition composts biol ssfmf     resi  res_MAD    rawp.BHStud
## 390   390          2       15   10  12.1 2.787154 4.673809 0.000002956642
##               adjp      bholm out_flag
## 390 0.000002956642 0.00119744  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ssfmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   9148.8130  1 121.75 < 0.00000000000000022 ***
## composts        11.2588  2 393.00            0.00001759 ***
## biol            12.5231  2 393.00            0.00000534 ***
## composts:biol    3.2855  4 393.01               0.01147 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 8.574444 0.0896445 121.7535 8.396981 8.751908 bB
0 15 9.127778 0.0896445 121.7535 8.950314 9.305242 aA
0 5 8.646667 0.0896445 121.7535 8.469203 8.824131 bB
10 0 9.194444 0.0896445 121.7535 9.016981 9.371908 aA
10 15 9.244318 0.0906677 125.5713 9.064884 9.423753 aA
10 5 9.291111 0.0896445 121.7535 9.113647 9.468575 aA
5 0 8.767778 0.0896445 121.7535 8.590314 8.945242 bB
5 15 9.293333 0.0896445 121.7535 9.115870 9.470797 aA
5 5 8.913333 0.0896445 121.7535 8.735869 9.090797 bB

p1d <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at physiological maturity (brix^{o})"
           , glab = "Biol"
           #, ylimits = c(0, 4, 1)
           , type = "line"
           )

p1d


mc_1d <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_d <- mc_1d %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at physiological maturity (brix^{o})"
           , glab = "Biol"
           , ylimits = c(0, 10, 2)
           , type = "bar"
           )

5.1.6 Titratable acidity of the fruit at physiological maturity (TAF_pm)

trait <- "atfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        atfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: atfmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   8570.0663  1  10.48 < 0.00000000000000022 ***
## composts        15.1416  2 394.00        0.000000461889 ***
## biol            19.8942  2 394.00        0.000000005875 ***
## composts:biol    3.2215  4 394.00               0.01277 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 1.365333 0.0147485 10.4798 1.332674 1.397992 aA
0 15 1.278000 0.0147485 10.4798 1.245341 1.310659 aB
0 5 1.298000 0.0147485 10.4798 1.265341 1.330659 aB
10 0 1.260444 0.0147485 10.4798 1.227786 1.293103 cA
10 15 1.235111 0.0147485 10.4798 1.202452 1.267770 bA
10 5 1.270889 0.0147485 10.4798 1.238230 1.303548 aA
5 0 1.313556 0.0147485 10.4798 1.280897 1.346214 bA
5 15 1.263889 0.0147485 10.4798 1.231230 1.296548 abB
5 5 1.301556 0.0147485 10.4798 1.268897 1.334214 aAB

p1e <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at physiological maturity ('%')"
           , glab = "Biol"
           #
           , type = "line"
           )

p1e


mc_1e <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_e <- mc_1e %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at physiological maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 1.5, 0.5)
           , type = "bar"
           )

5.1.7 Fruit dry matter percentage at physiological maturity (FDMP_pm)

trait <- "msfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        msfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: msfmf
##                        F Df Df.res                Pr(>F)    
## (Intercept)   17651.7307  1   20.4 < 0.00000000000000022 ***
## composts          3.9206  2  394.0               0.02061 *  
## biol             11.0365  2  394.0            0.00002169 ***
## composts:biol     2.3050  4  394.0               0.05777 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 19.26889 0.1450317 20.39742 18.96674 19.57104 bAB
0 15 19.57556 0.1450317 20.39742 19.27340 19.87771 cA
0 5 19.07333 0.1450317 20.39742 18.77118 19.37549 cB
10 0 20.02778 0.1450317 20.39742 19.72562 20.32993 aB
10 15 20.96133 0.1450317 20.39742 20.65918 21.26349 aA
10 5 20.00222 0.1450317 20.39742 19.70007 20.30438 aB
5 0 19.31778 0.1450317 20.39742 19.01562 19.61993 bB
5 15 20.21600 0.1450317 20.39742 19.91385 20.51815 bA
5 5 19.55744 0.1450317 20.39742 19.25529 19.85960 bB

p1f <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dry matter at physiological maturity ('%')"
           , glab = "Biol"
           #
           , type = "line"
           )

p1f


mc_1f <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_f <- mc_1f %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dry matter at physiological maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 24, 6)
           , type = "bar"
           )

5.1.8 Figure 3

Univariate analysis of the variables for determining the physiological maturity of the fruit at harvest time and for preventing handling damage during commercialization or industrial processes.

legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1e + theme(legend.position="none")
           , p1f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))
  
fig %>% 
  ggsave2(plot = ., "submission/Figure_3.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_3.jpg")

5.1.9 Supplementary Figure 1

Univariate analysis of the variables for determining the physiological maturity of the fruit at harvest time and for preventing handling damage during commercialization or industrial processes.

legend <- cowplot::get_plot_component(p1_a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1_a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_e + theme(legend.position="none")
           , p1_f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))
  
fig %>% 
  ggsave2(plot = ., "submission/Figure_S1.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_S1.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_S1.jpg")

5.1.10 Multivariate analysis

Principal Component Analysis (PCA) of quality traits to correlate with mango fruits at physiological maturity from compost and biol applications.

mv <- fisio %>% 
  select(-rpp) %>% 
  group_by(composts, biol) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", composts:biol, sep = "-") %>% 
  rename(Treat = treat
         ,PFCC_pm = pcfmf
         ,FF_pm = ffmf
         ,FPC_pm = cifmf
         ,SSCF_pm = ssfmf
         ,FpH_pm = phfmf
         ,TAF_pm = atfmf
         ,FDMP_pm = msfmf
         ,FPMI = imf) %>% 
  mutate(Treat = case_when(Treat == "0-0" ~ "T0",
                           Treat == "0-5" ~ "T1",
                           Treat == "0-10" ~ "T2",
                           Treat == "5-0" ~ "T3",
                           Treat == "5-5" ~ "T4",
                           Treat == "5-10" ~ "T5",
                           Treat == "15-0" ~ "T6",
                           Treat == "15-5" ~ "T7",
                           Treat == "15-10" ~ "T8")) %>% 
  column_to_rownames(var = "Treat")

pca <- mv %>% 
  PCA(scale.unit = T, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7  Dim.8
## Variance               6.64   0.92   0.30   0.11   0.03   0.01   0.00   0.00
## % of var.             82.98  11.50   3.69   1.38   0.32   0.11   0.02   0.00
## Cumulative % of var.  82.98  94.48  98.18  99.55  99.87  99.98 100.00 100.00
## 
## Individuals
##            Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## T0      |  4.21 | -3.95 26.12  0.88 |  0.91 10.01  0.05 |  1.15 49.38  0.07 |
## T1      |  2.39 | -2.11  7.49  0.78 |  0.51  3.20  0.05 | -0.80 24.02  0.11 |
## T2      |  1.63 |  1.37  3.14  0.70 | -0.81  7.95  0.25 |  0.03  0.03  0.00 |
## T3      |  3.09 | -2.93 14.39  0.90 |  0.15  0.27  0.00 | -0.77 22.04  0.06 |
## T4      |  0.97 | -0.87  1.26  0.81 | -0.16  0.32  0.03 | -0.03  0.04  0.00 |
## T5      |  2.01 |  1.60  4.27  0.63 | -1.15 15.99  0.33 |  0.34  4.22  0.03 |
## T6      |  0.94 |  0.24  0.09  0.06 | -0.79  7.60  0.71 |  0.08  0.23  0.01 |
## T7      |  2.11 |  1.98  6.54  0.87 | -0.67  5.49  0.10 |  0.03  0.03  0.00 |
## T8      |  5.10 |  4.68 36.72  0.84 |  2.02 49.17  0.16 | -0.02  0.01  0.00 |
## 
## Variables
##           Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## PFCC_pm |  0.96 14.00  0.93 | -0.05  0.31  0.00 | -0.15  7.30  0.02 |
## FF_pm   |  0.98 14.49  0.96 |  0.16  2.73  0.03 | -0.04  0.62  0.00 |
## FPC_pm  |  0.57  4.97  0.33 |  0.81 71.87  0.66 |  0.00  0.00  0.00 |
## SSCF_pm |  0.95 13.53  0.90 | -0.30  9.82  0.09 |  0.06  1.40  0.00 |
## FpH_pm  |  0.90 12.14  0.81 | -0.18  3.48  0.03 |  0.39 50.23  0.15 |
## TAF_pm  | -0.92 12.84  0.85 |  0.14  2.06  0.02 |  0.30 31.32  0.09 |
## FDMP_pm |  0.96 13.81  0.92 |  0.21  4.97  0.05 |  0.14  6.95  0.02 |
## FPMI    |  0.97 14.21  0.94 | -0.21  4.76  0.04 | -0.08  2.18  0.01 |


f2a <- fviz_pca_var(pca,
             col.var = "contrib", # Color by contributions to the PC
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
)

# plot.PCA(x = pca, choix = "var"
#                 , cex=0.5
#                 )

f2b <- fviz_pca_ind(pca,
                axes = c(1,2),
                geom.ind = "text",
                repel = FALSE,
             , habillage = 1) +
  theme(legend.position = "none")

# plot.PCA(x = pca, choix = "ind"
#                 , habillage = 1
#                 , invisible = c("ind")
#                 , cex=0.8
#                 , ylim = c(-3,3)
#                 ) 

5.1.11 Figure 4

Principal Component Analysis (PCA).

fig <- list(f2a, f2b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1.2, 1.1)
            )

fig %>% 
  ggsave2(plot = ., "submission/Figure_4.jpg", units = "cm"
         , width = 28
         , height = 13
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_4.eps", units = "cm"
         , width = 28
         , height = 13
         )

knitr::include_graphics("submission/Figure_4.jpg")

5.1.12 Supplementary Figure 3

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 90) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 15) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 


pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,)
  
}


plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))


ggsave2(plot = plot, "submission/Fig_S3.jpg", height = 20, width = 26, units = "cm")

ggsave2(plot = plot, "submission/Fig_S3.eps", height = 20, width = 26, units = "cm")

knitr::include_graphics("submission/Fig_S3.jpg")

5.2 Specific Objective 2

Demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at commercial maturity.

5.2.1 Fruit firmness at commercial maturity (FFCM)

trait <- "ffmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        ffmc        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ffmc
##                       F Df  Df.res                Pr(>F)    
## (Intercept)   1445.4047  1  79.714 < 0.00000000000000022 ***
## composts         9.4582  2 124.000             0.0001503 ***
## biol            12.3385  2 124.000            0.00001297 ***
## composts:biol    2.7839  4 124.000             0.0295392 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 3.546667 0.093288 79.71429 3.361007 3.732326 bB
0 15 4.120000 0.093288 79.71429 3.934341 4.305659 aA
0 5 3.813333 0.093288 79.71429 3.627674 3.998992 aAB
10 0 4.053333 0.093288 79.71429 3.867674 4.238993 aA
10 15 4.333333 0.093288 79.71429 4.147674 4.518993 aA
10 5 4.093333 0.093288 79.71429 3.907674 4.278993 aA
5 0 3.440000 0.093288 79.71429 3.254341 3.625659 bB
5 15 4.213333 0.093288 79.71429 4.027674 4.398993 aA
5 5 4.040000 0.093288 79.71429 3.854341 4.225659 aA

p2a <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at commercial maturity (kgf/cm^{2})"
           , glab = "Biol"
           # , ylimits = c(0, 6, 2)
           , type = "line"
           ) 

p2a


mc_2a <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_a <- mc_2a %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at commercial maturity (kgf/cm^{2})"
           , glab = "Biol"
           , ylimits = c(0, 5, 1)
           , type = "bar"
           )

5.2.2 Soluble solids content of the fruit at commercial maturity (SSCFCM)

trait <- "ssfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        ssfmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ssfmc
##                       F Df  Df.res                Pr(>F)    
## (Intercept)   9986.2968  1  79.714 < 0.00000000000000022 ***
## composts         7.9608  2 124.000             0.0005588 ***
## biol             3.2317  2 124.000             0.0428380 *  
## composts:biol    0.2479  4 124.000             0.9104665    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 14.92667 0.149369 79.71429 14.62940 15.22394 bB
0 15 15.61333 0.149369 79.71429 15.31606 15.91060 bA
0 5 14.84667 0.149369 79.71429 14.54940 15.14394 bB
10 0 15.44000 0.149369 79.71429 15.14273 15.73727 aB
10 15 16.12000 0.149369 79.71429 15.82273 16.41727 aA
10 5 15.46667 0.149369 79.71429 15.16940 15.76394 aB
5 0 15.04667 0.149369 79.71429 14.74940 15.34394 abB
5 15 15.72667 0.149369 79.71429 15.42940 16.02394 abA
5 5 15.22000 0.149369 79.71429 14.92273 15.51727 abB

p2b <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at commercial maturity (brix^{o})"
           # , ylimits = c(0, 18, 3)
           , type = "line"
           )

p2b


mc_2b <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_b <- mc_2b %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at commercial maturity (brix^{o})"
           , glab = "Biol"
           , ylimits = c(0, 18, 6)
           , type = "bar"
           )

5.2.3 Titratable acidity of the fruit at commercial maturity (TAFCM)

trait <- "atfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##    index repetition composts biol atfmc       resi   res_MAD   rawp.BHStud
## 4      4          1        0    0  0.50 -0.1157152 -4.533406 0.00000580400
## 5      5          1        0    0  0.50 -0.1157152 -4.533406 0.00000580400
## 18    18          1        5    0  0.74  0.1062848  4.163951 0.00003127878
## 27    27          3        5    0  0.50 -0.1006342 -3.942576 0.00008061124
##             adjp        bholm out_flag
## 4  0.00000580400 0.0007835399  OUTLIER
## 5  0.00000580400 0.0007835399  OUTLIER
## 18 0.00003127878 0.0041600772  OUTLIER
## 27 0.00008061124 0.0106406836  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: atfmc
##                       F Df  Df.res                Pr(>F)    
## (Intercept)   2079.9463  1   4.664          0.0000002314 ***
## composts        51.5834  2 120.023 < 0.00000000000000022 ***
## biol             3.1057  2 120.024             0.0484099 *  
## composts:biol    6.1259  4 120.008             0.0001608 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 0.6194568 0.0135827 4.663973 0.5837711 0.6551425 aA
0 15 0.5193333 0.0132175 4.191089 0.4832861 0.5553805 aB
0 5 0.6190321 0.0135726 4.653054 0.5833455 0.6547186 aA
10 0 0.5946667 0.0132175 4.191089 0.5586195 0.6307139 aA
10 15 0.4746667 0.0132175 4.191089 0.4386195 0.5107139 bC
10 5 0.5346667 0.0132175 4.191089 0.4986195 0.5707139 bB
5 0 0.5940000 0.0132175 4.191089 0.5579528 0.6300472 aA
5 15 0.5138667 0.0132175 4.191089 0.4778195 0.5499139 aC
5 5 0.5496667 0.0132175 4.191089 0.5136195 0.5857139 bB

p2c <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at commercial maturity ('%')"
           # , ylimits = c(0, 0.8, 0.2)
           , type = "line"
           ) 

p2c


mc_2c <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_c <- mc_2c %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at commercial maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 0.8, 0.2)
           , type = "bar"
           )

5.2.4 Fruit dehydration percentage at commercial maturity (FDPCM)

trait <- "pdfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        pdfmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: pdfmc
##                        F Df  Df.res                Pr(>F)    
## (Intercept)   12115.1009  1  35.333 < 0.00000000000000022 ***
## composts         18.2396  2 124.000          0.0000001138 ***
## biol              9.5482  2 124.000              0.000139 ***
## composts:biol     1.0347  4 124.000              0.392143    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 6.760667 0.0614223 35.33349 6.636015 6.885319 aA
0 15 6.301333 0.0614223 35.33349 6.176681 6.425985 aB
0 5 6.685333 0.0614223 35.33349 6.560682 6.809985 aA
10 0 6.408000 0.0614223 35.33349 6.283348 6.532652 bA
10 15 6.162667 0.0614223 35.33349 6.038015 6.287318 aB
10 5 6.375333 0.0614223 35.33349 6.250681 6.499985 bA
5 0 6.539333 0.0614223 35.33349 6.414682 6.663985 bA
5 15 6.236000 0.0614223 35.33349 6.111348 6.360652 aB
5 5 6.492000 0.0614223 35.33349 6.367348 6.616652 abA

p2d <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dehydration at commercial maturity ('%')"
           , glab = "Biol"
           # , ylimits = c(0, 8, 2)
           , type = "line"
           )

p2d


mc_2d <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_d <- mc_2d %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dehydration at commercial maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 8, 2)
           , type = "bar"
           )

5.2.5 Fruit pH at commercial maturity (FpHCM)

trait <- "phfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##    index repetition composts biol phfmc       resi   res_MAD   rawp.BHStud
## 27    27          3        5    0   3.8 -0.4029042 -4.421673 0.00000979398
##             adjp       bholm out_flag
## 27 0.00000979398 0.001322187  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: phfmc
##                        F Df  Df.res             Pr(>F)    
## (Intercept)   12371.3187  1   7.279 0.0000000000004991 ***
## composts         29.9322  2 123.011 0.0000000000255894 ***
## biol              0.9928  2 123.000          0.3734901    
## composts:biol     5.2737  4 123.004          0.0005922 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 4.332000 0.0389476 7.279143 4.240615 4.423385 aB
0 15 4.520000 0.0389476 7.279143 4.428615 4.611385 bA
0 5 4.203941 0.0397047 7.839443 4.112055 4.295828 bC
10 0 4.388000 0.0389476 7.279143 4.296615 4.479385 aB
10 15 4.692667 0.0389476 7.279143 4.601281 4.784052 aA
10 5 4.484000 0.0389476 7.279143 4.392615 4.575385 aB
5 0 4.369333 0.0389476 7.279143 4.277948 4.460719 aB
5 15 4.568667 0.0389476 7.279143 4.477281 4.660052 bA
5 5 4.429333 0.0389476 7.279143 4.337948 4.520719 aB

p2e <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pH at commercial maturity"
           # , ylimits = c(0, 6, 2)
           , type = "line"
           )

p2e


mc_2e <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_e <- mc_2e %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pH at commercial maturity"
           , glab = "Biol"
           , ylimits = c(0, 5.5, 1)
           , type = "bar"
           )

5.2.6 Fruit pulp color at commercial maturity (IFCCM)

trait <- "cifmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)


rmout$outliers
##  [1] index       repetition  composts    biol        cifmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: cifmc
##                      F Df Df.res                Pr(>F)    
## (Intercept)   968.1908  1  48.54 < 0.00000000000000022 ***
## composts       11.8649  2 124.00           0.000019281 ***
## biol           14.3983  2 124.00           0.000002383 ***
## composts:biol   3.8606  4 124.00              0.005438 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
0 0 3.066667 0.0985567 48.54019 2.868562 3.264771 bB
0 15 3.666667 0.0985567 48.54019 3.468562 3.864771 aA
0 5 3.593333 0.0985567 48.54019 3.395229 3.791438 aA
10 0 3.766667 0.0985567 48.54019 3.568562 3.964771 aA
10 15 3.800000 0.0985567 48.54019 3.601895 3.998105 aA
10 5 3.633333 0.0985567 48.54019 3.435229 3.831438 aA
5 0 3.566667 0.0985567 48.54019 3.368562 3.764771 aA
5 15 3.700000 0.0985567 48.54019 3.501895 3.898105 aA
5 5 3.633333 0.0985567 48.54019 3.435229 3.831438 aA

p2f <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pulp color at commercial maturity"
           # , ylimits = c(0, 5, 1)
           , type = "line"
           )

p2f


mc_2f <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_f <- mc_2f %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pH at commercial maturity"
           , glab = "Biol"
           , ylimits = c(0, 4.5, 1)
           , type = "bar"
           )

5.2.7 Figure 5

Univariate analysis of the most crucial variables for determining the commercial maturity of the fruit in the postharvest handling process.

legend <- cowplot::get_plot_component(p2a, 'guide-box-top', return_all = TRUE)

p2 <- list(p2a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2e + theme(legend.position="none")
           , p2f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p2, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_5.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_5.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_5.jpg")

5.2.8 Supplementary Figure 2

Univariate analysis of the most crucial variables for determining the commercial maturity of the fruit in the postharvest handling process.

legend <- cowplot::get_plot_component(p2a, 'guide-box-top', return_all = TRUE)

p2 <- list(p2_a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_e + theme(legend.position="none")
           , p2_f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p2, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_S2.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_S2.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_S2.jpg")

5.2.9 Multivariate analysis

Principal Component Analysis (PCA) of quality characteristics to correlate with mango fruits at commercial maturity from compost and biol applications.

mv <- consumo %>% 
  group_by(composts, biol) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", composts:biol, sep = "-") %>% 
  rename(Treat = treat
         ,FDP_cm = pdfmc
         ,FF_cm = ffmc
         ,FPC_cm = cifmc
         ,SSCF_cm = ssfmc
         ,FpH_cm = phfmc
         ,TAF_cm = atfmc
         ,FCMI = imf) %>% 
  mutate(Treat = case_when(Treat == "0-0" ~ "T0",
                           Treat == "0-5" ~ "T1",
                           Treat == "0-10" ~ "T2",
                           Treat == "5-0" ~ "T3",
                           Treat == "5-5" ~ "T4",
                           Treat == "5-10" ~ "T5",
                           Treat == "15-0" ~ "T6",
                           Treat == "15-5" ~ "T7",
                           Treat == "15-10" ~ "T8")) %>% 
  column_to_rownames(var = "Treat")
  
pca <- mv %>% 
  PCA(scale.unit = T, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7
## Variance               5.94   0.72   0.22   0.08   0.03   0.01   0.00
## % of var.             84.79  10.35   3.12   1.10   0.46   0.18   0.01
## Cumulative % of var.  84.79  95.14  98.26  99.36  99.81  99.99 100.00
## 
## Individuals
##            Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## T0      |  3.99 | -3.53 23.36  0.78 | -1.82 50.71  0.21 |  0.30  4.70  0.01 |
## T1      |  2.48 | -2.15  8.62  0.75 |  0.02  0.01  0.00 | -1.22 76.40  0.24 |
## T2      |  1.45 | -0.25  0.11  0.03 |  1.28 25.17  0.78 | -0.03  0.05  0.00 |
## T3      |  3.26 | -3.01 16.91  0.85 |  1.12 19.22  0.12 |  0.47 11.33  0.02 |
## T4      |  0.61 | -0.15  0.04  0.06 |  0.20  0.60  0.10 |  0.33  5.43  0.28 |
## T5      |  0.89 |  0.85  1.36  0.92 | -0.05  0.04  0.00 |  0.18  1.59  0.04 |
## T6      |  1.64 |  1.62  4.92  0.98 | -0.15  0.35  0.01 |  0.02  0.03  0.00 |
## T7      |  2.31 |  2.29  9.85  0.99 | -0.11  0.19  0.00 |  0.04  0.08  0.00 |
## T8      |  4.36 |  4.31 34.81  0.98 | -0.49  3.71  0.01 | -0.09  0.39  0.00 |
## 
## Variables
##           Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## FDP_cm  | -0.98 16.06  0.95 | -0.11  1.57  0.01 |  0.14  9.00  0.02 |
## FF_cm   |  0.89 13.39  0.79 |  0.22  6.42  0.05 |  0.39 71.20  0.16 |
## FPC_cm  |  0.71  8.50  0.50 |  0.68 64.34  0.47 | -0.15  9.80  0.02 |
## SSCF_cm |  0.98 16.24  0.96 | -0.06  0.49  0.00 | -0.04  0.73  0.00 |
## FpH_cm  |  0.94 14.82  0.88 | -0.30 12.42  0.09 | -0.14  8.48  0.02 |
## TAF_cm  | -0.95 15.29  0.91 |  0.25  8.69  0.06 | -0.03  0.35  0.00 |
## FCMI    |  0.97 15.70  0.93 | -0.21  6.07  0.04 |  0.03  0.44  0.00 |


f4a <- fviz_pca_var(pca,
             col.var = "contrib", # Color by contributions to the PC
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
)

# plot.PCA(x = pca, choix = "var"
#                 , cex=0.5
#                 )

f4b <- fviz_pca_ind(pca,
                axes = c(1,2),
                geom.ind = "text",
                repel = FALSE,
             , habillage = 1) +
  theme(legend.position = "none") +
  ylim(c(-3,3))

5.2.10 Figure 6

Principal Component Analysis (PCA).

fig <- list(f4a, f4b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1.2, 1.05)
            )
fig %>% 
  ggsave2(plot = ., "submission/Figure_6.jpg", units = "cm"
         , width = 28
         , height = 13
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_6.eps", units = "cm"
         , width = 28
         , height = 13
         )

knitr::include_graphics("submission/Figure_6.jpg")

5.2.11 Supplementary Figure 4

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 100) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 20) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 


pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,)
  
}


plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))

ggsave2(plot = plot, "submission/Fig_S4.jpg", height = 16, width = 25, units = "cm")

ggsave2(plot = plot, "submission/Fig_S4.eps", height = 16, width = 25, units = "cm")

knitr::include_graphics("submission/Fig_S4.jpg")

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 3

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8) + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8) +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8) +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 40)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top")

plot %>% 
  ggsave2(plot = ., "submission/weather.jpg", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/weather.jpg")